{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e3fc9a7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>职位名称</th>\n",
       "      <th>薪资范围</th>\n",
       "      <th>地点</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>学历要求</th>\n",
       "      <th>岗位标签</th>\n",
       "      <th>公司名称</th>\n",
       "      <th>公司类型</th>\n",
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       "      <th>城市</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>1万-2万</td>\n",
       "      <td>上海</td>\n",
       "      <td>不限</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['数据分析', '数据挖掘', '数据建模', 'SQL', 'Python']</td>\n",
       "      <td>好人生(上海)健康科技有限公司</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>1.2万-1.7万</td>\n",
       "      <td>上海-徐汇区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['Python', 'Tableau', '描述统计', 'R', 'Excel', 'P...</td>\n",
       "      <td>西安万兴服务外包有限责任公司</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>7千-9千</td>\n",
       "      <td>上海</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['数据分析']</td>\n",
       "      <td>上海乔治白实业有限公司</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>2万-3万</td>\n",
       "      <td>上海-杨浦区</td>\n",
       "      <td>不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>['Python', 'SQL', '回归分析', '方差分析']</td>\n",
       "      <td>上海八客信息科技有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>1.4万-2万</td>\n",
       "      <td>上海-宝山区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['SQL', 'Python', 'Pandas', '决策树', 'SVM RNN', ...</td>\n",
       "      <td>成都奋途商贸有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    职位名称       薪资范围      地点  工作经验 学历要求  \\\n",
       "0           0   数据分析师      1万-2万      上海    不限   硕士   \n",
       "1           1   数据分析师  1.2万-1.7万  上海-徐汇区  1-3年   本科   \n",
       "2           2  数据分析专员      7千-9千      上海  1-3年   本科   \n",
       "3           3   数据分析师      2万-3万  上海-杨浦区    不限   本科   \n",
       "4           4   数据分析师    1.4万-2万  上海-宝山区  1-3年   本科   \n",
       "\n",
       "                                                岗位标签             公司名称   公司类型  \\\n",
       "0          ['数据分析', '数据挖掘', '数据建模', 'SQL', 'Python']  好人生(上海)健康科技有限公司    民营    \n",
       "1  ['Python', 'Tableau', '描述统计', 'R', 'Excel', 'P...   西安万兴服务外包有限责任公司    NaN   \n",
       "2                                           ['数据分析']      上海乔治白实业有限公司  上市公司    \n",
       "3                  ['Python', 'SQL', '回归分析', '方差分析']     上海八客信息科技有限公司    民营    \n",
       "4  ['SQL', 'Python', 'Pandas', '决策树', 'SVM RNN', ...       成都奋途商贸有限公司    民营    \n",
       "\n",
       "          公司规模  省份  城市  \n",
       "0      20-99人   上海  上海  \n",
       "1      20-99人   上海  上海  \n",
       "2    100-299人   上海  上海  \n",
       "3  1000-9999人   上海  上海  \n",
       "4    100-299人   上海  上海  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Bar,Line,Tab,Pie\n",
    "import matplotlib.pyplot as plt\n",
    "from wordcloud import WordCloud\n",
    "from collections import Counter\n",
    "import seaborn as sns\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['font.serif']=['SimHei']\n",
    "df = pd.read_excel(r'实训大作业数据源.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "15879fae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "6258    False\n",
       "6259    False\n",
       "6260    False\n",
       "6261    False\n",
       "6262    False\n",
       "Length: 6263, dtype: bool"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据的重复值\n",
    "df.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0a780605",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "      Unnamed: 0   职位名称   薪资范围     地点   工作经验   学历要求   岗位标签   公司名称   公司类型  \\\n",
       "0          False  False  False  False  False  False  False  False  False   \n",
       "1          False  False  False  False  False  False  False  False   True   \n",
       "2          False  False  False  False  False  False  False  False  False   \n",
       "3          False  False  False  False  False  False  False  False  False   \n",
       "4          False  False  False  False  False  False  False  False  False   \n",
       "...          ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "6258       False  False  False  False  False  False  False  False  False   \n",
       "6259       False  False  False  False  False  False  False  False  False   \n",
       "6260       False  False  False  False  False  False  False  False  False   \n",
       "6261       False  False  False  False  False  False  False  False  False   \n",
       "6262       False  False  False  False  False  False  False  False  False   \n",
       "\n",
       "       公司规模     省份     城市  \n",
       "0     False  False  False  \n",
       "1     False  False  False  \n",
       "2     False  False  False  \n",
       "3     False  False  False  \n",
       "4     False  False  False  \n",
       "...     ...    ...    ...  \n",
       "6258  False  False  False  \n",
       "6259  False  False  False  \n",
       "6260  False  False  False  \n",
       "6261  False  False  False  \n",
       "6262  False  False  False  \n",
       "\n",
       "[6263 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检测数据的缺失值\n",
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c62db988",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Unnamed: 0  职位名称  薪资范围    地点  工作经验  学历要求  岗位标签  公司名称   公司类型  公司规模    省份  \\\n",
      "0           True  True  True  True  True  True  True  True   True  True  True   \n",
      "1           True  True  True  True  True  True  True  True  False  True  True   \n",
      "2           True  True  True  True  True  True  True  True   True  True  True   \n",
      "3           True  True  True  True  True  True  True  True   True  True  True   \n",
      "4           True  True  True  True  True  True  True  True   True  True  True   \n",
      "...          ...   ...   ...   ...   ...   ...   ...   ...    ...   ...   ...   \n",
      "6258        True  True  True  True  True  True  True  True   True  True  True   \n",
      "6259        True  True  True  True  True  True  True  True   True  True  True   \n",
      "6260        True  True  True  True  True  True  True  True   True  True  True   \n",
      "6261        True  True  True  True  True  True  True  True   True  True  True   \n",
      "6262        True  True  True  True  True  True  True  True   True  True  True   \n",
      "\n",
      "        城市  \n",
      "0     True  \n",
      "1     True  \n",
      "2     True  \n",
      "3     True  \n",
      "4     True  \n",
      "...    ...  \n",
      "6258  True  \n",
      "6259  True  \n",
      "6260  True  \n",
      "6261  True  \n",
      "6262  True  \n",
      "\n",
      "[6263 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "# 查看非缺失值\n",
    "not_null = df.notnull()\n",
    "print(not_null)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "389cc80f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0      0\n",
       "职位名称           44\n",
       "薪资范围           32\n",
       "地点             16\n",
       "工作经验            8\n",
       "学历要求           16\n",
       "岗位标签           38\n",
       "公司名称           53\n",
       "公司类型           36\n",
       "公司规模          107\n",
       "省份             10\n",
       "城市             21\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计缺失值\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0d7b96b8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6263 entries, 0 to 6262\n",
      "Data columns (total 12 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Unnamed: 0  6263 non-null   int64 \n",
      " 1   职位名称        6219 non-null   object\n",
      " 2   薪资范围        6231 non-null   object\n",
      " 3   地点          6247 non-null   object\n",
      " 4   工作经验        6255 non-null   object\n",
      " 5   学历要求        6247 non-null   object\n",
      " 6   岗位标签        6225 non-null   object\n",
      " 7   公司名称        6210 non-null   object\n",
      " 8   公司类型        6227 non-null   object\n",
      " 9   公司规模        6156 non-null   object\n",
      " 10  省份          6253 non-null   object\n",
      " 11  城市          6242 non-null   object\n",
      "dtypes: int64(1), object(11)\n",
      "memory usage: 587.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# 查看DataFrame的缺失值\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8a920c5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_39920\\3432907111.py:3: FutureWarning: The default value of numeric_only in DataFrame.sum is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  clean_null.sum()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Unnamed: 0    19609856\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 丢弃全部为缺失值的行\n",
    "clean_null = df.dropna(how='all')\n",
    "clean_null.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "20f007d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Unnamed: 0              职位名称       薪资范围       地点   工作经验 学历要求  \\\n",
      "1              1             数据分析师  1.2万-1.7万   上海-徐汇区   1-3年   本科   \n",
      "22            22              数据分析      6千-1万   上海-闵行区   1-3年   大专   \n",
      "27            27              数据分析    1.6万-2万       上海   3-5年   本科   \n",
      "28            28              数据分析    8千-1.3万  上海-浦东新区   3-5年   本科   \n",
      "33            33             数据分析岗       8千以下  上海-浦东新区     不限   本科   \n",
      "...          ...               ...        ...      ...    ...  ...   \n",
      "6140        6141       丽达茂—会员数据管理岗      4千-8千       青岛     不限   大专   \n",
      "6162        6163  信息分析助理 （市场研究员方向）    1万-1.2万   青岛-市南区     不限   本科   \n",
      "6216        6217             助理研究员      4千-6千      NaN   1年以下   本科   \n",
      "6251        6252               NaN      4千-8千       青岛   1-3年   大专   \n",
      "6257        6258              客研经理    8千-1.5万   青岛-李沧区  5-10年   本科   \n",
      "\n",
      "                                                   岗位标签             公司名称  \\\n",
      "1     ['Python', 'Tableau', '描述统计', 'R', 'Excel', 'P...   西安万兴服务外包有限责任公司   \n",
      "22                                                  NaN              NaN   \n",
      "27                                                  NaN       上海开州实业有限公司   \n",
      "28                                   ['数据分析', '销售业务数据']    上海美冠塔医院管理有限公司   \n",
      "33                                                  NaN     上海数治数据科技有限公司   \n",
      "...                                                 ...              ...   \n",
      "6140             ['物业客服', '现场客服', '零售客服', '倒班制', '全白班']  青岛丽达浮山后购物中心有限公司   \n",
      "6162          ['期货研究', '制造业', '基本面研究', '经济研究', '技术面研究']     青岛汇鑫国际贸易有限公司   \n",
      "6216                               ['市场调研分析', '数据统计分析']     山东洪鹏市场调查有限公司   \n",
      "6251             ['竞品分析', '市场研究', '用户调研', '用户分析', '全职']     青岛美集劳保用品有限公司   \n",
      "6257         ['行业研究', '市场研究', '用户调研', '用户分析', '市场信息收集']      保定市爱城置业有限公司   \n",
      "\n",
      "        公司类型         公司规模   省份   城市  \n",
      "1        NaN      20-99人    上海   上海  \n",
      "22       NaN          NaN  NaN   上海  \n",
      "27       民营     300-499人    上海   上海  \n",
      "28       NaN    100-299人    上海   上海  \n",
      "33       NaN      20-99人    上海   上海  \n",
      "...      ...          ...  ...  ...  \n",
      "6140  股份制企业   1000-9999人    山东  NaN  \n",
      "6162     NaN      20-99人    山东   青岛  \n",
      "6216     其它       20-99人    山东   青岛  \n",
      "6251     民营       20-99人    山东   青岛  \n",
      "6257                  NaN   山东   青岛  \n",
      "\n",
      "[354 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "# 查看有缺失值的数据\n",
    "has_null_row = df.isnull().any(axis=1)\n",
    "rows_with_null = df[has_null_row]\n",
    "print(rows_with_null)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "250bb9f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['数据分析师' '数据分析专员' 'data scientist（数据分析）' ... '证券投资咨询顾问' '财富管理师' '商业策划师'] \n",
      " ['1万-2万' '1.2万-1.7万' '7千-9千' '2万-3万' '1.4万-2万' '1.9万-2.2万' '1.5万-2万'\n",
      " '8千-1万' '1万-1.6万' '2.5万-5万' '1.3万-2.5万' '8千-1.6万' '1.8万-2.5万' '6千-8千'\n",
      " '6千-1万' '6千-1.2万' '1万-1.8万' '1.2万-2万' '1.6万-2万' '8千-1.3万' '3万-3.5万'\n",
      " '1.2万-1.5万' '3.5万-4万' '8千以下' '8千-1.2万' '7千-8千' '8千-1.1万' '1万-1.2万'\n",
      " '6千-9千' '1.5万-2.5万' '1万-1.5万' '1.2万-1.6万' '7千-1万' '7千-1.4万' '5千-7千'\n",
      " '9千-1.7万' '1万-1.7万' '4千-5千' '8千-1.4万' '9千-1.5万' '1.1万-2.2万' '5千-8千'\n",
      " '1.7万-2.7万' '1万-1.4万' '8千-1.5万' '1.3万-2万' '1.7万-2.6万' '1万-1.3万' '2万-2.5万'\n",
      " '1.3万-2.1万' '1.2万-2.2万' '7千-1.3万' '1.5万-2.4万' '2.5万-3.5万' '1.1万-1.6万'\n",
      " '9千-1.3万' '5千-1万' '3万-6万' '1.1万-1.5万' '1.3万-2.6万' '9千-1万' '1.5万-3万'\n",
      " '9千-1.2万' '1.5万-1.7万' '2.6万-3万' '3万-5万' '4万-7万' '9千-1.4万' '1.4万-1.6万'\n",
      " '1.4万-1.8万' '1.1万-1.4万' '1.5万-1.8万' '1.3万-1.5万' '1.4万-1.5万' '1.7万-2万'\n",
      " '2万-2.6万' '1.6万-2.5万' '1.3万-1.8万' '1.2万-2.1万' '1.5万-2.2万' '9千-1.1万'\n",
      " '1.6万-3万' '1.4万-1.9万' '1.8万-2.4万' '8千-9千' '2.5万-4万' '1.5万-2.3万'\n",
      " '1.7万-2.8万' '1.2万-1.8万' '2万-4万' '1.1万-1.8万' '1.4万-2.4万' '2.1万-2.2万'\n",
      " '7千-1.2万' '2.1万-2.8万' '1.5万-2.1万' '1.8万-3.5万' '8万-10万' '2万-3.5万'\n",
      " '1.2万-2.4万' '2万-2.2万' '1.6万-1.7万' '4.5万-7万' '2.2万-3.5万' '2.2万-2.8万'\n",
      " '4万-6万' '3万-4.5万' '1万-1.9万' '1.3万-1.6万' '3万-4万' '1.4万-2.8万' '1.7万-2.4万'\n",
      " '1.5万-2.8万' '9千-1.8万' '1.3万-1.7万' '1.7万-3万' '5千-9千' '1.1万-1.3万' '6万-8万'\n",
      " '1.1万-2万' '1.8万-2.2万' '2万-2.8万' '2.1万-3万' '2万-2.4万' '5千-6千' '9千-1.6万'\n",
      " '1.1万-1.9万' '1.4万-2.1万' '1.6万-2.1万' '3.5万-5.5万' '1.1万-1.2万' '1.8万-2.8万'\n",
      " '1.7万-2.5万' '1.2万-1.4万' '2.3万-3万' '2.2万-3万' '1.5万-1.6万' '1.8万-3万'\n",
      " '1.6万-1.8万' '2.1万-3.5万' '1.2万-1.3万' '2.5万-3万' '1.3万-2.2万' '1.6万-2.2万'\n",
      " '3.5万-5万' '6万-9万' '2.2万-4万' '2.1万-4万' '1千-2千' '1.6万-2.6万' '1.7万-1.8万'\n",
      " '4万-5万' '1.6万-1.9万' '4千-8千' '1.8万-2.6万' '7千-1.1万' '1万-1.1万' '6千-1.1万'\n",
      " '3千-4千' '1.2万-1.9万' '1.9万-3.5万' '4千-6千' '2.8万-3万' '1.6万-2.3万' '1.3万-2.3万'\n",
      " '1.5万-2.6万' '1.5万-2.7万' '1.7万-2.1万' '3千-6千' '2.2万-2.7万' '1.1万-2.1万'\n",
      " '2.5万-4.5万' '1.6万-2.8万' '2.8万-3.8万' '3万-5.5万' '1.7万-2.2万' '1.8万-2.3万'\n",
      " '1.4万-2.5万' '3.5万-6万' '4万-8万' '1.3万-1.4万' '2.4万-3.5万' '1.8万-2.7万'\n",
      " '1.8万-2万' '1.4万-2.2万' '3.5万-4.5万' '3.5万-7万' '1.5万-1.9万' '4千-7千'\n",
      " '1.7万-1.9万' '1.1万-1.7万' '12万-20万' nan '1.3万-2.4万' '3千-5千' '6千-7千'\n",
      " '2.6万-4万' '2.3万-3.5万' '1.7万-2.3万' '1.4万-1.7万' '1.6万-2.4万' '1.9万-2.5万'\n",
      " '2千-3千' '5万-8万' '1.2万-2.3万' '2万-2.9万' '1.3万-1.9万' '1.5万-2.9万' '1.9万-3万'\n",
      " '2.8万-4万' '1.4万-2.7万' '2千-4千' '1.6万-2.9万' '4.5千-9千' '3.5千-6千' '1.9万-2万'\n",
      " '1.6万-2.7万' '6万-10万' '2.8万-5.5万' '10万-20万' '1.9万-2.1万' '1.8万-3.6万'\n",
      " '4万-4.5万' '1.4万-2.3万'] \n",
      " ['1万-2万' '1.2万-1.7万' '7千-9千' '2万-3万' '1.4万-2万' '1.9万-2.2万' '1.5万-2万'\n",
      " '8千-1万' '1万-1.6万' '2.5万-5万' '1.3万-2.5万' '8千-1.6万' '1.8万-2.5万' '6千-8千'\n",
      " '6千-1万' '6千-1.2万' '1万-1.8万' '1.2万-2万' '1.6万-2万' '8千-1.3万' '3万-3.5万'\n",
      " '1.2万-1.5万' '3.5万-4万' '8千以下' '8千-1.2万' '7千-8千' '8千-1.1万' '1万-1.2万'\n",
      " '6千-9千' '1.5万-2.5万' '1万-1.5万' '1.2万-1.6万' '7千-1万' '7千-1.4万' '5千-7千'\n",
      " '9千-1.7万' '1万-1.7万' '4千-5千' '8千-1.4万' '9千-1.5万' '1.1万-2.2万' '5千-8千'\n",
      " '1.7万-2.7万' '1万-1.4万' '8千-1.5万' '1.3万-2万' '1.7万-2.6万' '1万-1.3万' '2万-2.5万'\n",
      " '1.3万-2.1万' '1.2万-2.2万' '7千-1.3万' '1.5万-2.4万' '2.5万-3.5万' '1.1万-1.6万'\n",
      " '9千-1.3万' '5千-1万' '3万-6万' '1.1万-1.5万' '1.3万-2.6万' '9千-1万' '1.5万-3万'\n",
      " '9千-1.2万' '1.5万-1.7万' '2.6万-3万' '3万-5万' '4万-7万' '9千-1.4万' '1.4万-1.6万'\n",
      " '1.4万-1.8万' '1.1万-1.4万' '1.5万-1.8万' '1.3万-1.5万' '1.4万-1.5万' '1.7万-2万'\n",
      " '2万-2.6万' '1.6万-2.5万' '1.3万-1.8万' '1.2万-2.1万' '1.5万-2.2万' '9千-1.1万'\n",
      " '1.6万-3万' '1.4万-1.9万' '1.8万-2.4万' '8千-9千' '2.5万-4万' '1.5万-2.3万'\n",
      " '1.7万-2.8万' '1.2万-1.8万' '2万-4万' '1.1万-1.8万' '1.4万-2.4万' '2.1万-2.2万'\n",
      " '7千-1.2万' '2.1万-2.8万' '1.5万-2.1万' '1.8万-3.5万' '8万-10万' '2万-3.5万'\n",
      " '1.2万-2.4万' '2万-2.2万' '1.6万-1.7万' '4.5万-7万' '2.2万-3.5万' '2.2万-2.8万'\n",
      " '4万-6万' '3万-4.5万' '1万-1.9万' '1.3万-1.6万' '3万-4万' '1.4万-2.8万' '1.7万-2.4万'\n",
      " '1.5万-2.8万' '9千-1.8万' '1.3万-1.7万' '1.7万-3万' '5千-9千' '1.1万-1.3万' '6万-8万'\n",
      " '1.1万-2万' '1.8万-2.2万' '2万-2.8万' '2.1万-3万' '2万-2.4万' '5千-6千' '9千-1.6万'\n",
      " '1.1万-1.9万' '1.4万-2.1万' '1.6万-2.1万' '3.5万-5.5万' '1.1万-1.2万' '1.8万-2.8万'\n",
      " '1.7万-2.5万' '1.2万-1.4万' '2.3万-3万' '2.2万-3万' '1.5万-1.6万' '1.8万-3万'\n",
      " '1.6万-1.8万' '2.1万-3.5万' '1.2万-1.3万' '2.5万-3万' '1.3万-2.2万' '1.6万-2.2万'\n",
      " '3.5万-5万' '6万-9万' '2.2万-4万' '2.1万-4万' '1千-2千' '1.6万-2.6万' '1.7万-1.8万'\n",
      " '4万-5万' '1.6万-1.9万' '4千-8千' '1.8万-2.6万' '7千-1.1万' '1万-1.1万' '6千-1.1万'\n",
      " '3千-4千' '1.2万-1.9万' '1.9万-3.5万' '4千-6千' '2.8万-3万' '1.6万-2.3万' '1.3万-2.3万'\n",
      " '1.5万-2.6万' '1.5万-2.7万' '1.7万-2.1万' '3千-6千' '2.2万-2.7万' '1.1万-2.1万'\n",
      " '2.5万-4.5万' '1.6万-2.8万' '2.8万-3.8万' '3万-5.5万' '1.7万-2.2万' '1.8万-2.3万'\n",
      " '1.4万-2.5万' '3.5万-6万' '4万-8万' '1.3万-1.4万' '2.4万-3.5万' '1.8万-2.7万'\n",
      " '1.8万-2万' '1.4万-2.2万' '3.5万-4.5万' '3.5万-7万' '1.5万-1.9万' '4千-7千'\n",
      " '1.7万-1.9万' '1.1万-1.7万' '12万-20万' nan '1.3万-2.4万' '3千-5千' '6千-7千'\n",
      " '2.6万-4万' '2.3万-3.5万' '1.7万-2.3万' '1.4万-1.7万' '1.6万-2.4万' '1.9万-2.5万'\n",
      " '2千-3千' '5万-8万' '1.2万-2.3万' '2万-2.9万' '1.3万-1.9万' '1.5万-2.9万' '1.9万-3万'\n",
      " '2.8万-4万' '1.4万-2.7万' '2千-4千' '1.6万-2.9万' '4.5千-9千' '3.5千-6千' '1.9万-2万'\n",
      " '1.6万-2.7万' '6万-10万' '2.8万-5.5万' '10万-20万' '1.9万-2.1万' '1.8万-3.6万'\n",
      " '4万-4.5万' '1.4万-2.3万'] \n",
      " ['上海' '上海-徐汇区' '上海-杨浦区' '上海-宝山区' '上海-浦东新区' '上海-黄浦区' '上海-长宁区' '上海-静安区'\n",
      " '上海-闵行区' '上海-普陀区' '上海-奉贤区' '上海-松江区' '上海-虹口区' '上海-青浦区' '上海-嘉定区' '上海-金山区'\n",
      " '北京-东城区' '北京-海淀区' '北京' '北京-通州区' '北京-大兴区' '北京-西城区' '北京-丰台区' '北京-朝阳区'\n",
      " '北京-昌平区' '北京-石景山区' '北京-顺义区' '北京-密云区' '北京-延庆区' '北京-门头沟区' nan '南京-江宁区'\n",
      " '南京-秦淮区' '南京' '南京-鼓楼区' '南京-建邺区' '南京-栖霞区' '南京-玄武区' '南京-雨花台区' '南京-浦口区'\n",
      " '南京-溧水区' '南京-高淳区' '厦门-思明区' '厦门' '厦门-湖里区' '厦门-海沧区' '厦门-同安区' '厦门-集美区'\n",
      " '厦门-翔安区' '大连-中山区' '大连-甘井子区' '大连-西岗区' '大连-沙河口区' '大连' '大连-金州区' '大连-高新园区'\n",
      " '大连-旅顺口区' '天津-西青区' '天津-南开区' '天津-东丽区' '天津-和平区' '天津-武清区' '天津-滨海新区' '天津-河东区'\n",
      " '天津' '天津-河北区' '天津-河西区' '天津-津南区' '天津-北辰区' '天津-红桥区' '天津-宁河区' '宁波-鄞州区'\n",
      " '宁波-慈溪市' '宁波' '宁波-江北区' '宁波-北仑区' '宁波-海曙区' '宁波-镇海区' '广州-天河区' '广州-番禺区'\n",
      " '广州-黄埔区' '广州-白云区' '广州' '广州-海珠区' '广州-荔湾区' '广州-越秀区' '广州-南沙区' '广州-花都区'\n",
      " '成都-崇州市' '成都-武侯区' '成都-锦江区' '成都' '成都-双流区' '成都-成华区' '成都-青羊区' '成都-金牛区'\n",
      " '成都-高新西区' '成都-天府新区' '成都-温江区' '成都-新都区' '成都-彭州市' '成都-高新区' '成都-新津区' '成都-郫都区'\n",
      " '成都-龙泉驿区' '无锡-江阴市' '无锡' '无锡-新吴区' '无锡-锡山区' '无锡-梁溪区' '无锡-宜兴市' '无锡-滨湖区'\n",
      " '无锡-惠山区' '杭州-上城区' '杭州-滨江区' '杭州-拱墅区' '杭州-萧山区' '杭州' '杭州-临平区' '杭州-西湖区'\n",
      " '杭州-建德市' '杭州-钱塘区' '杭州-余杭区' '杭州-临安区' '武汉-江汉区' '武汉-洪山区' '武汉-江夏区' '武汉'\n",
      " '武汉-武昌区' '武汉-江岸区' '武汉-东西湖区' '武汉-汉阳区' '武汉-硚口区' '武汉-蔡甸区' '武汉-东湖新技术开发区'\n",
      " '武汉-汉南区' '武汉-黄陂区' '沈阳' '沈阳-大东区' '沈阳-浑南区' '沈阳-铁西区' '沈阳-和平区' '沈阳-于洪区'\n",
      " '沈阳-沈河区' '沈阳-皇姑区' '沈阳-沈北新区' '济南-历下区' '济南-槐荫区' '济南-历城区' '济南-市中区' '济南'\n",
      " '济南-天桥区' '济南-高新区' '济南-莱芜区' '济南-章丘区' '济南-长清区' '深圳-龙岗区' '深圳-福田区' '深圳-南山区'\n",
      " '深圳-宝安区' '深圳' '深圳-罗湖区' '深圳-龙华区' '深圳-坪山区' '深圳-光明区' '福州-台江区' '福州-马尾区'\n",
      " '福州-鼓楼区' '福州-闽侯县' '福州' '福州-仓山区' '福州-晋安区' '福州-长乐区' '苏州-昆山市' '苏州-吴中区'\n",
      " '苏州-姑苏区' '苏州-虎丘区' '苏州-常熟市' '苏州' '苏州-工业园区' '苏州-张家港市' '苏州-高新区' '苏州-吴江区'\n",
      " '苏州-相城区' '苏州-太仓市' '西安-雁塔区' '西安-碑林区' '西安-新城区' '西安-未央区' '西安-莲湖区' '西安'\n",
      " '西安-临潼区' '西安-长安区' '西安-高新技术产业开发区' '西安-灞桥区' '西安-高陵区' '西安-国际港务区' '西安-曲江新区'\n",
      " '郑州-管城回族区' '郑州-中原区' '郑州-金水区' '郑州-惠济区' '郑州-二七区' '郑州' '郑州-郑东新区 ' '郑州-中牟县'\n",
      " '郑州-经开区' '郑州-登封市' '郑州-高新区' '重庆-江北区' '重庆-大渡口区' '重庆-渝北区' '重庆-渝中区' '重庆-沙坪坝区'\n",
      " '重庆-南岸区' '重庆' '重庆-九龙坡区' '重庆-永川区' '重庆-合川区' '长春' '长春-朝阳区' '长春-宽城区' '长春-南关区'\n",
      " '长春-二道区' '长春-绿园区' '长春-经济开发区' '长沙-芙蓉区' '长沙-岳麓区' '长沙' '长沙-雨花区' '长沙-开福区'\n",
      " '长沙-天心区' '长沙-长沙县' '长沙-宁乡市' '长沙-望城区' '青岛-市南区' '青岛-市北区' '青岛-黄岛区' '青岛-李沧区'\n",
      " '青岛-城阳区' '青岛' '青岛-崂山区' '青岛-平度市' '青岛-胶州市' '青岛-即墨区'] \n",
      " ['不限' '1-3年' '5-10年' '3-5年' '1年以下' '无经验' '10年以上' nan] \n",
      " ['硕士' '本科' '大专' '学历不限' '高中' '博士' nan '中专/中技'] \n",
      " [\"['数据分析', '数据挖掘', '数据建模', 'SQL', 'Python']\"\n",
      " \"['Python', 'Tableau', '描述统计', 'R', 'Excel', 'Power-BI']\" \"['数据分析']\" ...\n",
      " \"['基金数据分析', '银行数据分析', '信托数据分析', '资产管理数据分析', '经济分析', '风险分析', '保理数据分析', '理财数据分析']\"\n",
      " \"['证券从业资格证', '证券研究', '投融资研究']\" \"['商业策划', '商业数据分析', '可研报告']\"] \n",
      " ['好人生(上海)健康科技有限公司' '西安万兴服务外包有限责任公司' '上海乔治白实业有限公司' ... '青岛卓云海智医疗科技有限公司'\n",
      " '青岛智聘企业管理咨询有限公司' '陆玛文旅景观设计'] \n",
      " ['民营 ' nan '上市公司 ' '合资 ' '外商独资 ' '国企 ' '社会团体 ' '股份制企业 ' '其它 ' ' ' '事业单位 '\n",
      " '港澳台公司 ' '律师事务所 ' '医院 ' '学校/下级学院 ' '代表处 '] \n",
      " ['20-99人 ' '100-299人 ' '1000-9999人 ' '500-999人 ' '10000人以上 ' '300-499人 '\n",
      " nan '20人以下 '] \n",
      " ['上海' nan '北京' '江苏' '福建' '辽宁' '天津' '浙江' '广东' '四川' '湖北' '山东' '陕西' '河南' '重庆'\n",
      " '吉林' '湖南'] \n",
      " ['上海' nan '北京' '南京' '厦门' '大连' '天津' '宁波' '广州' '成都' '无锡' '杭州' '武汉' '沈阳' '济南'\n",
      " '深圳' '福州' '苏州' '西安' '郑州' '重庆' '长春' '长沙' '青岛']\n"
     ]
    }
   ],
   "source": [
    "# 每列数据的唯一值\n",
    "print(df['职位名称'].unique(),\"\\n\",\n",
    "      df['薪资范围'].unique(),\"\\n\",\n",
    "      df['薪资范围'].unique(),\"\\n\",\n",
    "      df['地点'].unique(),\"\\n\",\n",
    "      df['工作经验'].unique(),\"\\n\",\n",
    "      df['学历要求'].unique(),\"\\n\",\n",
    "      df['岗位标签'].unique(),\"\\n\",\n",
    "      df['公司名称'].unique(),\"\\n\",\n",
    "      df['公司类型'].unique(),\"\\n\",\n",
    "      df['公司规模'].unique(),\"\\n\",\n",
    "      df['省份'].unique(),\"\\n\",\n",
    "      df['城市'].unique()\n",
    "     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c8208842",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>职位名称</th>\n",
       "      <th>薪资范围</th>\n",
       "      <th>地点</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>学历要求</th>\n",
       "      <th>岗位标签</th>\n",
       "      <th>公司名称</th>\n",
       "      <th>公司类型</th>\n",
       "      <th>公司规模</th>\n",
       "      <th>省份</th>\n",
       "      <th>城市</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>1万-2万</td>\n",
       "      <td>上海</td>\n",
       "      <td>不限</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['数据分析', '数据挖掘', '数据建模', 'SQL', 'Python']</td>\n",
       "      <td>好人生(上海)健康科技有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>1.2万-1.7万</td>\n",
       "      <td>上海-徐汇区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['Python', 'Tableau', '描述统计', 'R', 'Excel', 'P...</td>\n",
       "      <td>西安万兴服务外包有限责任公司</td>\n",
       "      <td>其它</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>7千-9千</td>\n",
       "      <td>上海</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['数据分析']</td>\n",
       "      <td>上海乔治白实业有限公司</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>2万-3万</td>\n",
       "      <td>上海-杨浦区</td>\n",
       "      <td>不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>['Python', 'SQL', '回归分析', '方差分析']</td>\n",
       "      <td>上海八客信息科技有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>1.4万-2万</td>\n",
       "      <td>上海-宝山区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['SQL', 'Python', 'Pandas', '决策树', 'SVM RNN', ...</td>\n",
       "      <td>成都奋途商贸有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>data scientist（数据分析）</td>\n",
       "      <td>1.9万-2.2万</td>\n",
       "      <td>上海-浦东新区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['Python']</td>\n",
       "      <td>宜通华瑞</td>\n",
       "      <td>民营</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>销售数据分析Contingent Business Support</td>\n",
       "      <td>1.5万-2万</td>\n",
       "      <td>上海-黄浦区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['Power BI', '制药/医疗', '数据分析']</td>\n",
       "      <td>科锐国际</td>\n",
       "      <td>合资</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Data Analysis数据分析专员</td>\n",
       "      <td>8千-1万</td>\n",
       "      <td>上海-长宁区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['数据分析', '销售业务数据']</td>\n",
       "      <td>上海柏迦科技有限公司</td>\n",
       "      <td>合资</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>数据分析助理经理(J12409)</td>\n",
       "      <td>1万-1.6万</td>\n",
       "      <td>上海-长宁区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['数据分析', '销售业务数据']</td>\n",
       "      <td>广州市斯凯奇商业有限公司</td>\n",
       "      <td>外商独资</td>\n",
       "      <td>500-999人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>数据分析JC</td>\n",
       "      <td>2.5万-5万</td>\n",
       "      <td>上海</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['数据分析', '数据集市', '需求分析', 'SQL']</td>\n",
       "      <td>中邮邮惠万家银行有限责任公司</td>\n",
       "      <td>国企</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                               职位名称       薪资范围       地点   工作经验  \\\n",
       "0           0                              数据分析师      1万-2万       上海     不限   \n",
       "1           1                              数据分析师  1.2万-1.7万   上海-徐汇区   1-3年   \n",
       "2           2                             数据分析专员      7千-9千       上海   1-3年   \n",
       "3           3                              数据分析师      2万-3万   上海-杨浦区     不限   \n",
       "4           4                              数据分析师    1.4万-2万   上海-宝山区   1-3年   \n",
       "5           5               data scientist（数据分析）  1.9万-2.2万  上海-浦东新区   1-3年   \n",
       "6           6  销售数据分析Contingent Business Support    1.5万-2万   上海-黄浦区   1-3年   \n",
       "7           7                Data Analysis数据分析专员      8千-1万   上海-长宁区   1-3年   \n",
       "8           8                   数据分析助理经理(J12409)    1万-1.6万   上海-长宁区  5-10年   \n",
       "9           9                             数据分析JC    2.5万-5万       上海   3-5年   \n",
       "\n",
       "  学历要求                                               岗位标签             公司名称  \\\n",
       "0   硕士          ['数据分析', '数据挖掘', '数据建模', 'SQL', 'Python']  好人生(上海)健康科技有限公司   \n",
       "1   本科  ['Python', 'Tableau', '描述统计', 'R', 'Excel', 'P...   西安万兴服务外包有限责任公司   \n",
       "2   本科                                           ['数据分析']      上海乔治白实业有限公司   \n",
       "3   本科                  ['Python', 'SQL', '回归分析', '方差分析']     上海八客信息科技有限公司   \n",
       "4   本科  ['SQL', 'Python', 'Pandas', '决策树', 'SVM RNN', ...       成都奋途商贸有限公司   \n",
       "5   本科                                         ['Python']             宜通华瑞   \n",
       "6   本科                      ['Power BI', '制药/医疗', '数据分析']             科锐国际   \n",
       "7   本科                                 ['数据分析', '销售业务数据']       上海柏迦科技有限公司   \n",
       "8   本科                                 ['数据分析', '销售业务数据']     广州市斯凯奇商业有限公司   \n",
       "9   本科                    ['数据分析', '数据集市', '需求分析', 'SQL']   中邮邮惠万家银行有限责任公司   \n",
       "\n",
       "    公司类型         公司规模  省份  城市  \n",
       "0    民营       20-99人   上海  上海  \n",
       "1     其它      20-99人   上海  上海  \n",
       "2  上市公司     100-299人   上海  上海  \n",
       "3    民营   1000-9999人   上海  上海  \n",
       "4    民营     100-299人   上海  上海  \n",
       "5    民营   1000-9999人   上海  上海  \n",
       "6    合资   1000-9999人   上海  上海  \n",
       "7    合资     100-299人   上海  上海  \n",
       "8  外商独资     500-999人   上海  上海  \n",
       "9    国企     100-299人   上海  上海  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对缺失值进行处理  \n",
    "mode_value1 = df['职位名称'].mode()[0]\n",
    "df['职位名称'].fillna(mode_value1,inplace=True)\n",
    "df['薪资范围']=df['薪资范围'].fillna(method='ffill',inplace=False)\n",
    "mode_value2 = df['地点'].mode()[0]\n",
    "df['地点'].fillna(mode_value2,inplace=True)\n",
    "# 根据职位称呼填充工作经验 shift()用于获取上一条记录中的工作经验，并将其填充到当前行的工作经验中 1上 -1下\n",
    "df['工作经验'].fillna(df['工作经验'].shift(1),inplace=True),\n",
    "df['学历要求'].fillna(method='ffill',inplace=True)\n",
    "df['岗位标签'].fillna(method='ffill',inplace=True)\n",
    "df['公司名称'].fillna(method='bfill',inplace=True)\n",
    "df['公司类型'].fillna(value='其它',inplace=True)\n",
    "df['公司规模'].fillna(value='20人以下',inplace=True)\n",
    "df['省份'].fillna(df['省份'].shift(1),inplace=True)\n",
    "df['城市'].fillna(df['城市'].shift(-1),inplace=True)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ecf9700a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0    0\n",
       "职位名称          0\n",
       "薪资范围          0\n",
       "地点            0\n",
       "工作经验          0\n",
       "学历要求          0\n",
       "岗位标签          0\n",
       "公司名称          0\n",
       "公司类型          0\n",
       "公司规模          0\n",
       "省份            0\n",
       "城市            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检验缺失值是否填充成功\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ea58d543",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>职位名称</th>\n",
       "      <th>薪资范围</th>\n",
       "      <th>地点</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>学历要求</th>\n",
       "      <th>岗位标签</th>\n",
       "      <th>公司名称</th>\n",
       "      <th>公司类型</th>\n",
       "      <th>公司规模</th>\n",
       "      <th>省份</th>\n",
       "      <th>城市</th>\n",
       "      <th>薪资上限</th>\n",
       "      <th>薪资下限</th>\n",
       "      <th>处理后工作经验</th>\n",
       "      <th>薪资均值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>1万-2万</td>\n",
       "      <td>上海</td>\n",
       "      <td>不限</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['数据分析', '数据挖掘', '数据建模', 'SQL', 'Python']</td>\n",
       "      <td>好人生(上海)健康科技有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>1.2万-1.7万</td>\n",
       "      <td>上海-徐汇区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['Python', 'Tableau', '描述统计', 'R', 'Excel', 'P...</td>\n",
       "      <td>西安万兴服务外包有限责任公司</td>\n",
       "      <td>其它</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>7千-9千</td>\n",
       "      <td>上海</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['数据分析']</td>\n",
       "      <td>上海乔治白实业有限公司</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>2万-3万</td>\n",
       "      <td>上海-杨浦区</td>\n",
       "      <td>不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>['Python', 'SQL', '回归分析', '方差分析']</td>\n",
       "      <td>上海八客信息科技有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>1.4万-2万</td>\n",
       "      <td>上海-宝山区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['SQL', 'Python', 'Pandas', '决策树', 'SVM RNN', ...</td>\n",
       "      <td>成都奋途商贸有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>data scientist（数据分析）</td>\n",
       "      <td>1.9万-2.2万</td>\n",
       "      <td>上海-浦东新区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['Python']</td>\n",
       "      <td>宜通华瑞</td>\n",
       "      <td>民营</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>销售数据分析Contingent Business Support</td>\n",
       "      <td>1.5万-2万</td>\n",
       "      <td>上海-黄浦区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['Power BI', '制药/医疗', '数据分析']</td>\n",
       "      <td>科锐国际</td>\n",
       "      <td>合资</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Data Analysis数据分析专员</td>\n",
       "      <td>8千-1万</td>\n",
       "      <td>上海-长宁区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['数据分析', '销售业务数据']</td>\n",
       "      <td>上海柏迦科技有限公司</td>\n",
       "      <td>合资</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>数据分析助理经理(J12409)</td>\n",
       "      <td>1万-1.6万</td>\n",
       "      <td>上海-长宁区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['数据分析', '销售业务数据']</td>\n",
       "      <td>广州市斯凯奇商业有限公司</td>\n",
       "      <td>外商独资</td>\n",
       "      <td>500-999人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>13000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>数据分析JC</td>\n",
       "      <td>2.5万-5万</td>\n",
       "      <td>上海</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['数据分析', '数据集市', '需求分析', 'SQL']</td>\n",
       "      <td>中邮邮惠万家银行有限责任公司</td>\n",
       "      <td>国企</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                               职位名称       薪资范围       地点   工作经验  \\\n",
       "0           0                              数据分析师      1万-2万       上海     不限   \n",
       "1           1                              数据分析师  1.2万-1.7万   上海-徐汇区   1-3年   \n",
       "2           2                             数据分析专员      7千-9千       上海   1-3年   \n",
       "3           3                              数据分析师      2万-3万   上海-杨浦区     不限   \n",
       "4           4                              数据分析师    1.4万-2万   上海-宝山区   1-3年   \n",
       "5           5               data scientist（数据分析）  1.9万-2.2万  上海-浦东新区   1-3年   \n",
       "6           6  销售数据分析Contingent Business Support    1.5万-2万   上海-黄浦区   1-3年   \n",
       "7           7                Data Analysis数据分析专员      8千-1万   上海-长宁区   1-3年   \n",
       "8           8                   数据分析助理经理(J12409)    1万-1.6万   上海-长宁区  5-10年   \n",
       "9           9                             数据分析JC    2.5万-5万       上海   3-5年   \n",
       "\n",
       "  学历要求                                               岗位标签             公司名称  \\\n",
       "0   硕士          ['数据分析', '数据挖掘', '数据建模', 'SQL', 'Python']  好人生(上海)健康科技有限公司   \n",
       "1   本科  ['Python', 'Tableau', '描述统计', 'R', 'Excel', 'P...   西安万兴服务外包有限责任公司   \n",
       "2   本科                                           ['数据分析']      上海乔治白实业有限公司   \n",
       "3   本科                  ['Python', 'SQL', '回归分析', '方差分析']     上海八客信息科技有限公司   \n",
       "4   本科  ['SQL', 'Python', 'Pandas', '决策树', 'SVM RNN', ...       成都奋途商贸有限公司   \n",
       "5   本科                                         ['Python']             宜通华瑞   \n",
       "6   本科                      ['Power BI', '制药/医疗', '数据分析']             科锐国际   \n",
       "7   本科                                 ['数据分析', '销售业务数据']       上海柏迦科技有限公司   \n",
       "8   本科                                 ['数据分析', '销售业务数据']     广州市斯凯奇商业有限公司   \n",
       "9   本科                    ['数据分析', '数据集市', '需求分析', 'SQL']   中邮邮惠万家银行有限责任公司   \n",
       "\n",
       "    公司类型         公司规模  省份  城市     薪资上限     薪资下限  处理后工作经验     薪资均值  \n",
       "0    民营       20-99人   上海  上海  10000.0  20000.0      1.0  15000.0  \n",
       "1     其它      20-99人   上海  上海  12000.0  17000.0      2.0  14500.0  \n",
       "2  上市公司     100-299人   上海  上海   7000.0   9000.0      2.0   8000.0  \n",
       "3    民营   1000-9999人   上海  上海  20000.0  30000.0      1.0  25000.0  \n",
       "4    民营     100-299人   上海  上海  14000.0  20000.0      2.0  17000.0  \n",
       "5    民营   1000-9999人   上海  上海  19000.0  22000.0      2.0  20500.0  \n",
       "6    合资   1000-9999人   上海  上海  15000.0  20000.0      2.0  17500.0  \n",
       "7    合资     100-299人   上海  上海   8000.0  10000.0      2.0   9000.0  \n",
       "8  外商独资     500-999人   上海  上海  10000.0  16000.0      7.5  13000.0  \n",
       "9    国企     100-299人   上海  上海  25000.0  50000.0      4.0  37500.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#新增一列表示薪资均值\n",
    "data2 = df[df['薪资范围']!='面议']\n",
    "data2 = data2[data2['薪资范围'].astype(str).str.contains('-')]\n",
    "def split_scale(x):\n",
    "    if isinstance(x,str):\n",
    "        return x.split('-')\n",
    "    else:\n",
    "        return [x,x]\n",
    "data2['薪资上限'] = data2.薪资范围.apply(lambda x: x.split('-')[0])\n",
    "data2['薪资下限'] = data2.薪资范围.apply(lambda x: x.split('-')[1])\n",
    "\n",
    "def salary_handle(word):\n",
    "    if word[-1] == '万':\n",
    "        num = float(word.strip('万'))*10000\n",
    "    elif  word[-1] == '千':\n",
    "        num = float(word.strip('千'))*1000\n",
    "    else:\n",
    "        num = float(word)\n",
    "    return num\n",
    "\n",
    "# 假设工作经验数据所在的列名为'工作经验'\n",
    "data2['工作经验'] = data2['工作经验'].astype(str)  # 将数据转换为字符串类型\n",
    "\n",
    "# 定义处理工作经验的函数\n",
    "def experience_handle(experience):\n",
    "    if experience == '无经验':\n",
    "        return 0\n",
    "    elif experience == '不限':\n",
    "        return 1  # 使用-1代表不限\n",
    "    elif '以上' in experience:\n",
    "        return int(experience.split('年')[0])  # 提取年份上限\n",
    "    elif '以下' in experience:\n",
    "        return int(experience.split('年')[0])  # 提取年份下限\n",
    "    elif '-' in experience:\n",
    "        min_exp, max_exp = experience.split('-')\n",
    "        max_exp = max_exp[:-1]  # 去掉最大经验值中的单位\n",
    "        return (int(min_exp) + int(max_exp)) / 2  # 取平均值作为工作经验\n",
    "    else:\n",
    "        return int(experience.split('年')[0])\n",
    "\n",
    "# 应用处理函数到'工作经验'列\n",
    "data2['处理后工作经验'] = data2['工作经验'].apply(experience_handle)\n",
    "data2['薪资上限']=data2.薪资上限.apply(lambda x: salary_handle(x))\n",
    "data2['薪资下限']=data2.薪资下限.apply(lambda x: salary_handle(x))\n",
    "data2['薪资均值']=round((data2.薪资上限+data2.薪资下限)/2,2)\n",
    "data2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0bd8a754",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "194     45000.0\n",
       "218     45000.0\n",
       "223     40000.0\n",
       "224     55000.0\n",
       "255     45000.0\n",
       "         ...   \n",
       "6006    42500.0\n",
       "6015    45000.0\n",
       "6065    45000.0\n",
       "6067    50000.0\n",
       "6262    50000.0\n",
       "Name: 薪资均值, Length: 90, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对异常值（薪资均值）的检测\n",
    "def outRange(S):\n",
    "    blidx = (S.mean()-3*S.std()>S)|(S.mean()+3 * S.std()<S)\n",
    "    idx=np.arange(S.shape[0])[blidx]\n",
    "    outRange = S.iloc[idx]\n",
    "    return outRange\n",
    "outier = outRange(data2['薪资均值'])\n",
    "outier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "65a7e9fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "归一化薪资均值之前的数据：\n",
      " [15000. 14500.  8000. ... 11500.  8500. 50000.]\n",
      "标准化薪资均值之前的数据：\n",
      " [15000. 14500.  8000. ... 11500.  8500. 50000.]\n",
      "归一化之后薪资均值的数据：\n",
      " [0.0851735  0.08201893 0.04100946 ... 0.06309148 0.04416404 0.30599369]\n",
      "标准化之后薪资均值的数据：\n",
      " [ 0.18447772  0.12227477 -0.68636358 ... -0.25094293 -0.62416063\n",
      "  4.53868422]\n",
      "归一化之前工作经验的数据：\n",
      " [1.  2.  2.  ... 2.  4.  7.5]\n",
      "归一化之后工作经验的数据：\n",
      " [0.1  0.2  0.2  ... 0.2  0.4  0.75]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 进行归一化和标准化\n",
    "def MinMaxScale(data):\n",
    "    data=(data-data.min())/(data.max()-data.min())\n",
    "    return data\n",
    "def StandardScale(data):\n",
    "    data = (data-data.mean())/data.std()\n",
    "    return data\n",
    "salary=np.array(data2['薪资均值'])\n",
    "print(\"归一化薪资均值之前的数据：\\n\",salary)\n",
    "print(\"标准化薪资均值之前的数据：\\n\",salary)\n",
    "new_salary1= MinMaxScale(salary)\n",
    "new_salary2=StandardScale(salary)\n",
    "print(\"归一化之后薪资均值的数据：\\n\",new_salary1)\n",
    "print(\"标准化之后薪资均值的数据：\\n\",new_salary2)\n",
    "experience=np.array(data2['处理后工作经验'])\n",
    "print(\"归一化之前工作经验的数据：\\n\",experience)\n",
    "new_experience= MinMaxScale(experience)\n",
    "print(\"归一化之后工作经验的数据：\\n\",new_experience)\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 绘制散点图\n",
    "plt.scatter(new_experience, new_salary1, color='blue', label='数据点')\n",
    "\n",
    "# 进行线性回归拟合\n",
    "coefficients = np.polyfit(new_experience, new_salary1, 1)\n",
    "polynomial = np.poly1d(coefficients)\n",
    "x = np.linspace(0, 1, 100)  # 生成用于绘制回归线的 x 值\n",
    "y = polynomial(x)\n",
    "\n",
    "# 绘制回归线\n",
    "plt.plot(x, y, color='red', label='线性回归')\n",
    "\n",
    "# 添加图例和标签\n",
    "plt.legend()\n",
    "plt.xlabel('归一化后的工作经验')\n",
    "plt.ylabel('归一化后的薪资')\n",
    "plt.title('归一化后的工作经验与薪资的线性回归')\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5302b034",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/v5/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "    <style>\n",
       "        .tab {\n",
       "            overflow: hidden;\n",
       "            border: 1px solid #ccc;\n",
       "            background-color: #f1f1f1;\n",
       "        }\n",
       "\n",
       "        .tab button {\n",
       "            background-color: inherit;\n",
       "            float: left;\n",
       "            border: none;\n",
       "            outline: none;\n",
       "            cursor: pointer;\n",
       "            padding: 12px 16px;\n",
       "            transition: 0.3s;\n",
       "        }\n",
       "\n",
       "        .tab button:hover {\n",
       "            background-color: #ddd;\n",
       "        }\n",
       "\n",
       "        .tab button.active {\n",
       "            background-color: #ccc;\n",
       "        }\n",
       "\n",
       "        .chart-container {\n",
       "            display: block;\n",
       "        }\n",
       "\n",
       "        .chart-container:nth-child(n+2) {\n",
       "            display: none;\n",
       "        }\n",
       "    </style>\n",
       "<div class=\"tab\">\n",
       "            <button class=\"tablinks\" onclick=\"showChart(event, 'cace91929f2e49318b99ff35c631d607')\">折线图</button>\n",
       "            <button class=\"tablinks\" onclick=\"showChart(event, '2b2964b24bd04c5e88cd0451dbf418a4')\">饼图</button>\n",
       "    </div>\n",
       "\n",
       "        <div id=\"cace91929f2e49318b99ff35c631d607\" class=\"chart-container\" style=\"width:900px; height:500px;\"></div>\n",
       "        <div id=\"2b2964b24bd04c5e88cd0451dbf418a4\" class=\"chart-container\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_cace91929f2e49318b99ff35c631d607 = echarts.init(\n",
       "                    document.getElementById('cace91929f2e49318b99ff35c631d607'), 'white', {renderer: 'canvas'});\n",
       "                var option_cace91929f2e49318b99ff35c631d607 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"aria\": {\n",
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       "        \"#5470c6\",\n",
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       "        \"#fac858\",\n",
       "        \"#ee6666\",\n",
       "        \"#73c0de\",\n",
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       "            \"smooth\": false,\n",
       "            \"clip\": true,\n",
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       "            \"data\": [\n",
       "                [\n",
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       "                    19\n",
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       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"enterable\": false,\n",
       "        \"confine\": false,\n",
       "        \"appendToBody\": false,\n",
       "        \"transitionDuration\": 0.4,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
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       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5,\n",
       "        \"order\": \"seriesAsc\"\n",
       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
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       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
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       "            },\n",
       "            \"data\": [\n",
       "                \"\\u4e2d\\u4e13/\\u4e2d\\u6280\",\n",
       "                \"\\u535a\\u58eb\",\n",
       "                \"\\u5927\\u4e13\",\n",
       "                \"\\u5b66\\u5386\\u4e0d\\u9650\",\n",
       "                \"\\u672c\\u79d1\",\n",
       "                \"\\u7855\\u58eb\",\n",
       "                \"\\u9ad8\\u4e2d\"\n",
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       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
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       "            \"splitNumber\": 5,\n",
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       "            \"splitLine\": {\n",
       "                \"show\": true,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
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       "        {\n",
       "            \"show\": true,\n",
       "            \"text\": \"\\u5b66\\u5386\\u8981\\u6c42\\u5206\\u5e03\",\n",
       "            \"target\": \"blank\",\n",
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       "            \"itemGap\": 10,\n",
       "            \"textAlign\": \"auto\",\n",
       "            \"textVerticalAlign\": \"auto\",\n",
       "            \"triggerEvent\": false\n",
       "        }\n",
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       "                var chart_2b2964b24bd04c5e88cd0451dbf418a4 = echarts.init(\n",
       "                    document.getElementById('2b2964b24bd04c5e88cd0451dbf418a4'), 'white', {renderer: 'canvas'});\n",
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       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"aria\": {\n",
       "        \"enabled\": false\n",
       "    },\n",
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       "        \"#5470c6\",\n",
       "        \"#91cc75\",\n",
       "        \"#fac858\",\n",
       "        \"#ee6666\",\n",
       "        \"#73c0de\",\n",
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       "        \"#fc8452\",\n",
       "        \"#9a60b4\",\n",
       "        \"#ea7ccc\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"pie\",\n",
       "            \"colorBy\": \"data\",\n",
       "            \"legendHoverLink\": true,\n",
       "            \"selectedMode\": false,\n",
       "            \"selectedOffset\": 10,\n",
       "            \"clockwise\": true,\n",
       "            \"startAngle\": 90,\n",
       "            \"minAngle\": 0,\n",
       "            \"minShowLabelAngle\": 0,\n",
       "            \"avoidLabelOverlap\": true,\n",
       "            \"stillShowZeroSum\": true,\n",
       "            \"percentPrecision\": 2,\n",
       "            \"showEmptyCircle\": true,\n",
       "            \"emptyCircleStyle\": {\n",
       "                \"color\": \"lightgray\",\n",
       "                \"borderColor\": \"#000\",\n",
       "                \"borderWidth\": 0,\n",
       "                \"borderType\": \"solid\",\n",
       "                \"borderDashOffset\": 0,\n",
       "                \"borderCap\": \"butt\",\n",
       "                \"borderJoin\": \"bevel\",\n",
       "                \"borderMiterLimit\": 10,\n",
       "                \"opacity\": 1\n",
       "            },\n",
       "            \"data\": [\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5927\\u4e13\",\n",
       "                    \"value\": 1666\n",
       "                },\n",
       "                {\n",
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       "                    \"value\": 126\n",
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       "                {\n",
       "                    \"name\": \"\\u672c\\u79d1\",\n",
       "                    \"value\": 3900\n",
       "                },\n",
       "                {\n",
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       "                    \"value\": 465\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9ad8\\u4e2d\",\n",
       "                    \"value\": 13\n",
       "                }\n",
       "            ],\n",
       "            \"radius\": [\n",
       "                \"0%\",\n",
       "                \"75%\"\n",
       "            ],\n",
       "            \"center\": [\n",
       "                \"50%\",\n",
       "                \"50%\"\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"margin\": 8,\n",
       "                \"formatter\": \"{b}: {c}\"\n",
       "            },\n",
       "            \"labelLine\": {\n",
       "                \"show\": true,\n",
       "                \"showAbove\": false,\n",
       "                \"length\": 15,\n",
       "                \"length2\": 15,\n",
       "                \"smooth\": false,\n",
       "                \"minTurnAngle\": 90,\n",
       "                \"maxSurfaceAngle\": 90\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
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       "        }\n",
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       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u4e2d\\u4e13/\\u4e2d\\u6280\",\n",
       "                \"\\u535a\\u58eb\",\n",
       "                \"\\u5927\\u4e13\",\n",
       "                \"\\u5b66\\u5386\\u4e0d\\u9650\",\n",
       "                \"\\u672c\\u79d1\",\n",
       "                \"\\u7855\\u58eb\",\n",
       "                \"\\u9ad8\\u4e2d\"\n",
       "            ],\n",
       "            \"selected\": {},\n",
       "            \"show\": true,\n",
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       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14,\n",
       "            \"backgroundColor\": \"transparent\",\n",
       "            \"borderColor\": \"#ccc\",\n",
       "            \"borderWidth\": 1,\n",
       "            \"borderRadius\": 0,\n",
       "            \"pageButtonItemGap\": 5,\n",
       "            \"pageButtonPosition\": \"end\",\n",
       "            \"pageFormatter\": \"{current}/{total}\",\n",
       "            \"pageIconColor\": \"#2f4554\",\n",
       "            \"pageIconInactiveColor\": \"#aaa\",\n",
       "            \"pageIconSize\": 15,\n",
       "            \"animationDurationUpdate\": 800,\n",
       "            \"selector\": false,\n",
       "            \"selectorPosition\": \"auto\",\n",
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       "            \"selectorButtonGap\": 10\n",
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       "    \"tooltip\": {\n",
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       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
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       "        \"showContent\": true,\n",
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       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"enterable\": false,\n",
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       "        \"padding\": 5,\n",
       "        \"order\": \"seriesAsc\"\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"text\": \"\\u5b66\\u5386\\u8981\\u6c42\\u5206\\u5e03\",\n",
       "            \"target\": \"blank\",\n",
       "            \"subtarget\": \"blank\",\n",
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       "            \"itemGap\": 10,\n",
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       "            \"triggerEvent\": false\n",
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       "                chart_2b2964b24bd04c5e88cd0451dbf418a4.setOption(option_2b2964b24bd04c5e88cd0451dbf418a4);\n",
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       "    </script>\n",
       "<script>\n",
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       "            if(containers.length > 0) {\n",
       "                containers[0].style.display = \"block\";\n",
       "            }\n",
       "        })()\n",
       "\n",
       "        function showChart(evt, chartID) {\n",
       "            let containers = document.getElementsByClassName(\"chart-container\");\n",
       "            for (let i = 0; i < containers.length; i++) {\n",
       "                containers[i].style.display = \"none\";\n",
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       "\n",
       "            let tablinks = document.getElementsByClassName(\"tablinks\");\n",
       "            for (let i = 0; i < tablinks.length; i++) {\n",
       "                tablinks[i].className = \"tablinks\";\n",
       "            }\n",
       "\n",
       "            document.getElementById(chartID).style.display = \"block\";\n",
       "            evt.currentTarget.className += \" active\";\n",
       "        }\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x1e0c19241d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对学历要求进行独热编码\n",
    "encoded_data = pd.get_dummies(data2['学历要求'])\n",
    "\n",
    "# 统计学历要求的数量\n",
    "count = encoded_data.sum()\n",
    "# 将编码后的数据与原始数据合并\n",
    "data3 = pd.concat([data2, encoded_data], axis=1)\n",
    "data3.to_excel(r'实训大作业数据源（清洗和预处理后）.xlsx',index=False)\n",
    "line_chart = (\n",
    "    Line()  # 使用Line类\n",
    "    .add_xaxis(count.index.tolist())\n",
    "    .add_yaxis(\"数量\", count.tolist())\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(title=\"学历要求分布\"))\n",
    ")\n",
    "# 绘制饼图\n",
    "pie_chart = (\n",
    "    Pie()\n",
    "    .add(\"\", [list(z) for z in zip(count.index.tolist(), count.tolist())])\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(title=\"学历要求分布\"))\n",
    "    .set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}: {c}\"))\n",
    ")\n",
    "# 创建 Tab，并添加两个图表\n",
    "tab = Tab()\n",
    "tab.add(line_chart, \"折线图\")  # 将图表对象传递给 add 方法\n",
    "tab.add(pie_chart, \"饼图\")    # 将图表对象传递给 add 方法\n",
    "\n",
    "# 渲染出页面\n",
    "tab.render_notebook()\n",
    "\n",
    "#从柱状图可以看出在数据分析岗位中学历要求为本科的比较多，高中学历的最少，可以看出学历要求为本科的具有更好的竞争力，该岗位对学历要求还是有的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7583900d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        薪资均值   数量\n",
      "0    12500.0  355\n",
      "1     9000.0  348\n",
      "2    15000.0  346\n",
      "3     7000.0  329\n",
      "4     8000.0  295\n",
      "..       ...  ...\n",
      "78   90000.0    1\n",
      "79    6750.0    1\n",
      "80    4750.0    1\n",
      "81   80000.0    1\n",
      "82  150000.0    1\n",
      "\n",
      "[83 rows x 2 columns]\n",
      "薪资均值最小值： 1500.0\n",
      "薪资均值最大值： 160000.0\n",
      "每个薪资均值区间的分布如下\n",
      " (5000, 10000]      2292\n",
      "(10000, 15000]     1849\n",
      "(20000, 160000]     906\n",
      "(15000, 20000]      899\n",
      "(1500, 5000]        261\n",
      "Name: 薪资均值, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 使用value_counts()函数统计每个薪资均值的个数，并按降序排列\n",
    "count = data2['薪资均值'].value_counts().sort_values(ascending=False)\n",
    "# 将结果转换为DataFrame格式\n",
    "result = pd.DataFrame({'薪资均值': count.index, '数量': count.values})\n",
    "# 打印结果\n",
    "# print(\"薪资均值的数量分布：\\n\")\n",
    "print(result)\n",
    "print(\"薪资均值最小值：\",data2['薪资均值'].min())\n",
    "print(\"薪资均值最大值：\",data2['薪资均值'].max())\n",
    "\n",
    "#等宽法\n",
    "bins=[1500,5000,10000,15000,20000,160000]\n",
    "salary_cut=pd.cut(data2['薪资均值'],bins)\n",
    "#统计每个薪资均值区间的分布\n",
    "# print(\"每个薪资均值区间的分布如下\")\n",
    "print(\"每个薪资均值区间的分布如下\\n\",pd.value_counts(salary_cut))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e4507930",
   "metadata": {},
   "outputs": [
    {
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       "        <div id=\"b801efecdebc491da8c552b6bd168463\" style=\"width:900px; height:500px;\"></div>\n",
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       "                \"borderCap\": \"butt\",\n",
       "                \"borderJoin\": \"bevel\",\n",
       "                \"borderMiterLimit\": 10,\n",
       "                \"opacity\": 1\n",
       "            },\n",
       "            \"data\": [\n",
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       "                    \"name\": \"(5000, 10000]\",\n",
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       "                {\n",
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       "                    \"value\": 1849\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"(20000, 160000]\",\n",
       "                    \"value\": 906\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"(15000, 20000]\",\n",
       "                    \"value\": 899\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"(1500, 5000]\",\n",
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       "                }\n",
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       "                \"75%\"\n",
       "            ],\n",
       "            \"center\": [\n",
       "                \"50%\",\n",
       "                \"50%\"\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"margin\": 8,\n",
       "                \"formatter\": \"{b}: {d}%\"\n",
       "            },\n",
       "            \"labelLine\": {\n",
       "                \"show\": true,\n",
       "                \"showAbove\": false,\n",
       "                \"length\": 15,\n",
       "                \"length2\": 15,\n",
       "                \"smooth\": false,\n",
       "                \"minTurnAngle\": 90,\n",
       "                \"maxSurfaceAngle\": 90\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"(5000, 10000]\",\n",
       "                \"(10000, 15000]\",\n",
       "                \"(20000, 160000]\",\n",
       "                \"(15000, 20000]\",\n",
       "                \"(1500, 5000]\"\n",
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       "            \"selected\": {},\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
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       "            \"itemHeight\": 14,\n",
       "            \"backgroundColor\": \"transparent\",\n",
       "            \"borderColor\": \"#ccc\",\n",
       "            \"borderWidth\": 1,\n",
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       "            \"pageButtonItemGap\": 5,\n",
       "            \"pageButtonPosition\": \"end\",\n",
       "            \"pageFormatter\": \"{current}/{total}\",\n",
       "            \"pageIconColor\": \"#2f4554\",\n",
       "            \"pageIconInactiveColor\": \"#aaa\",\n",
       "            \"pageIconSize\": 15,\n",
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       "            \"selector\": false,\n",
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       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"enterable\": false,\n",
       "        \"confine\": false,\n",
       "        \"appendToBody\": false,\n",
       "        \"transitionDuration\": 0.4,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5,\n",
       "        \"order\": \"seriesAsc\"\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"text\": \"\\u85aa\\u8d44\\u5747\\u503c\\u533a\\u95f4\\u5206\\u5e03\",\n",
       "            \"target\": \"blank\",\n",
       "            \"subtarget\": \"blank\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"textAlign\": \"auto\",\n",
       "            \"textVerticalAlign\": \"auto\",\n",
       "            \"triggerEvent\": false\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_b801efecdebc491da8c552b6bd168463.setOption(option_b801efecdebc491da8c552b6bd168463);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x1e0c48c1cd0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计每个薪资均值区间的分布\n",
    "value_counts = pd.value_counts(salary_cut)\n",
    "\n",
    "# 绘制饼状图\n",
    "pie_data = [(str(index), value) for index, value in value_counts.items()]\n",
    "\n",
    "pie = (\n",
    "    Pie()\n",
    "    .add(\"\", pie_data)\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(title=\"薪资均值区间分布\"))\n",
    "    .set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}: {d}%\"))\n",
    ")\n",
    "\n",
    "# 显示图形\n",
    "pie.render_notebook()\n",
    "\n",
    "#从下面的饼状图可以看出，薪资分布在5000-10000的数量较多，其次是分布在10000-15000，薪资分布最少的是在薪资范围为1500-5000。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4666dbc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
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       "</script>\n",
       "\n",
       "        <div id=\"3bcf538dc08d43e9b7b3b49c9cb15a55\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
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       "    \"animationThreshold\": 2000,\n",
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       "    \"yAxis\": [\n",
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       "            \"nameLocation\": \"end\",\n",
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       "    \"title\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"text\": \"\\u5168\\u56fd\\u5404\\u57ce\\u5e02\\u6570\\u636e\\u5206\\u6790\\u5c97\\u5e73\\u5747\\u85aa\\u8d44\\u6c34\\u5e73\\u6392\\u540d\",\n",
       "            \"target\": \"blank\",\n",
       "            \"subtarget\": \"blank\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"textAlign\": \"auto\",\n",
       "            \"textVerticalAlign\": \"auto\",\n",
       "            \"triggerEvent\": false\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_3bcf538dc08d43e9b7b3b49c9cb15a55.setOption(option_3bcf538dc08d43e9b7b3b49c9cb15a55);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x1e0c49e5d50>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_salary = data2[['城市','薪资均值']].groupby('城市').mean().round(0).sort_values(by = '薪资均值',ascending = True).reset_index()\n",
    "\n",
    "# 将城市和薪资均值提取为列表\n",
    "city = city_salary['城市'].tolist()\n",
    "salary = city_salary['薪资均值'].tolist()\n",
    "\n",
    "# 使用pyecharts绘制图表\n",
    "bar = (\n",
    "    Bar()\n",
    "    .add_xaxis(city)\n",
    "    .add_yaxis(\"平均薪资\", salary)\n",
    "    .reversal_axis()\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"全国各城市数据分析岗平均薪资水平排名\"),\n",
    "        xaxis_opts=opts.AxisOpts(name=\"平均薪资\"),\n",
    "        yaxis_opts=opts.AxisOpts(name=\"城市\"),\n",
    "    )\n",
    ")\n",
    "\n",
    "bar.render_notebook() # 在Jupyter Notebook中渲染图表\n",
    "# 查看各个城市的薪资水平可以发现，北上广深身为经济最发达的四个一线城市，薪资水平也是最高的。数据分析岗薪资最高的城市是上海，而四个城市中薪资最低的城市是广州。南京和苏杭二州的薪资是紧随其后的，而青岛的薪资均值位于最后。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4be2f215",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/v5/echarts.min'\n",
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       "\n",
       "        <div id=\"f42fb5f445e74a2e900633282d1188a3\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_f42fb5f445e74a2e900633282d1188a3 = echarts.init(\n",
       "                    document.getElementById('f42fb5f445e74a2e900633282d1188a3'), 'white', {renderer: 'canvas'});\n",
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       "                942,\n",
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       "            \"pageIconInactiveColor\": \"#aaa\",\n",
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       "        \"order\": \"seriesAsc\"\n",
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       "    \"xAxis\": [\n",
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       "                \"rotate\": -45\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": true,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"\\u5317\\u4eac\",\n",
       "                \"\\u4e0a\\u6d77\",\n",
       "                \"\\u6df1\\u5733\",\n",
       "                \"\\u6210\\u90fd\",\n",
       "                \"\\u5e7f\\u5dde\",\n",
       "                \"\\u676d\\u5dde\",\n",
       "                \"\\u6b66\\u6c49\",\n",
       "                \"\\u5357\\u4eac\",\n",
       "                \"\\u90d1\\u5dde\",\n",
       "                \"\\u6d4e\\u5357\",\n",
       "                \"\\u5929\\u6d25\",\n",
       "                \"\\u897f\\u5b89\",\n",
       "                \"\\u957f\\u6c99\",\n",
       "                \"\\u9752\\u5c9b\",\n",
       "                \"\\u91cd\\u5e86\",\n",
       "                \"\\u798f\\u5dde\",\n",
       "                \"\\u53a6\\u95e8\",\n",
       "                \"\\u5927\\u8fde\",\n",
       "                \"\\u82cf\\u5dde\",\n",
       "                \"\\u6c88\\u9633\",\n",
       "                \"\\u65e0\\u9521\",\n",
       "                \"\\u957f\\u6625\",\n",
       "                \"\\u5b81\\u6ce2\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"name\": \"\\u9700\\u6c42\\u6570\\u91cf\",\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": true,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"text\": \"\\u5404\\u57ce\\u5e02\\u5c97\\u4f4d\\u9700\\u6c42\",\n",
       "            \"target\": \"blank\",\n",
       "            \"subtarget\": \"blank\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"textAlign\": \"auto\",\n",
       "            \"textVerticalAlign\": \"auto\",\n",
       "            \"triggerEvent\": false\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_f42fb5f445e74a2e900633282d1188a3.setOption(option_f42fb5f445e74a2e900633282d1188a3);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x1e0c31f9750>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计各城市的岗位需求数量\n",
    "city_counts = data2['城市'].value_counts()\n",
    "\n",
    "# 创建柱状图对象，并设置x轴和y轴数据\n",
    "bar = (\n",
    "    Bar()\n",
    "    .add_xaxis(city_counts.index.tolist())\n",
    "    .add_yaxis('岗位需求', city_counts.values.tolist())\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title='各城市岗位需求'),\n",
    "        xaxis_opts=opts.AxisOpts(axislabel_opts={\"rotate\": -45}),\n",
    "        yaxis_opts=opts.AxisOpts(name='需求数量'),\n",
    "    )\n",
    ")\n",
    "\n",
    "# 渲染并显示图表\n",
    "bar.render_notebook()\n",
    "\n",
    "# 通过全国各地数据分析岗岗位需求排名可以发现，北京上海的岗位需求是最多的。其次是深圳。在北上广深四个一线城市中，广州的数据分析岗位需求是最少的。而成都的数据分析岗的岗位需求量近几年增长较快，甚至超过了广州。其次是杭州、武汉、南京等城市。\n",
    "# 因此要找数据分析岗工作或者实习的小伙伴，还是推荐去北上广深这样的一线城市。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5229af5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 提取岗位标签列的数据\n",
    "labels = data2['岗位标签'].apply(lambda x: x.strip(\"[]\").replace(\"'\", \"\").split(', ')).tolist()\n",
    "\n",
    "# # 将嵌套的列表展开成一维列表\n",
    "labels_flat = [label for sublist in labels for label in sublist]\n",
    "\n",
    "# 统计每个单词的出现次数\n",
    "# word_counts = Counter(labels_flat)\n",
    "\n",
    "# 将列表转换为 Series 对象\n",
    "labels_flat_series = pd.Series(labels_flat)\n",
    "\n",
    "# 使用 value_counts() 统计每个单词的出现次数\n",
    "word_counts = labels_flat_series.value_counts()\n",
    "\n",
    "# 创建WordCloud对象并生成词云图数据，设置权重为单词出现次数\n",
    "wordcloud = WordCloud(font_path='SimHei.ttf', background_color='white').generate_from_frequencies(word_counts)\n",
    "\n",
    "# 使用matplotlib绘制词云图\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis('off')\n",
    "plt.show()\n",
    "\n",
    "#从该词云图中可以看出求职者应该掌握几种比较重要的技术，数据分析岗位要求最多的技术是数据分析，其次是Python、sql等技术"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e52f7055",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "本科       3900\n",
       "大专       1666\n",
       "硕士        465\n",
       "学历不限      126\n",
       "博士         28\n",
       "中专/中技      19\n",
       "高中         13\n",
       "Name: 学历要求, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2['学历要求'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "093dc0d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/v5/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"eb9a18055a814e38957733e57aed91e6\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_eb9a18055a814e38957733e57aed91e6 = echarts.init(\n",
       "                    document.getElementById('eb9a18055a814e38957733e57aed91e6'), 'white', {renderer: 'canvas'});\n",
       "                var option_eb9a18055a814e38957733e57aed91e6 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"aria\": {\n",
       "        \"enabled\": false\n",
       "    },\n",
       "    \"color\": [\n",
       "        \"#5470c6\",\n",
       "        \"#91cc75\",\n",
       "        \"#fac858\",\n",
       "        \"#ee6666\",\n",
       "        \"#73c0de\",\n",
       "        \"#3ba272\",\n",
       "        \"#fc8452\",\n",
       "        \"#9a60b4\",\n",
       "        \"#ea7ccc\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"pie\",\n",
       "            \"colorBy\": \"data\",\n",
       "            \"legendHoverLink\": true,\n",
       "            \"selectedMode\": false,\n",
       "            \"selectedOffset\": 10,\n",
       "            \"clockwise\": true,\n",
       "            \"startAngle\": 90,\n",
       "            \"minAngle\": 0,\n",
       "            \"minShowLabelAngle\": 0,\n",
       "            \"avoidLabelOverlap\": true,\n",
       "            \"stillShowZeroSum\": true,\n",
       "            \"percentPrecision\": 2,\n",
       "            \"showEmptyCircle\": true,\n",
       "            \"emptyCircleStyle\": {\n",
       "                \"color\": \"lightgray\",\n",
       "                \"borderColor\": \"#000\",\n",
       "                \"borderWidth\": 0,\n",
       "                \"borderType\": \"solid\",\n",
       "                \"borderDashOffset\": 0,\n",
       "                \"borderCap\": \"butt\",\n",
       "                \"borderJoin\": \"bevel\",\n",
       "                \"borderMiterLimit\": 10,\n",
       "                \"opacity\": 1\n",
       "            },\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"\\u7855\\u58eb\",\n",
       "                    \"value\": 465\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u672c\\u79d1\",\n",
       "                    \"value\": 3900\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5927\\u4e13\",\n",
       "                    \"value\": 1666\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5b66\\u5386\\u4e0d\\u9650\",\n",
       "                    \"value\": 126\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9ad8\\u4e2d\",\n",
       "                    \"value\": 13\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u535a\\u58eb\",\n",
       "                    \"value\": 28\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e2d\\u4e13/\\u4e2d\\u6280\",\n",
       "                    \"value\": 19\n",
       "                }\n",
       "            ],\n",
       "            \"radius\": [\n",
       "                \"40%\",\n",
       "                \"55%\"\n",
       "            ],\n",
       "            \"center\": [\n",
       "                240,\n",
       "                220\n",
       "            ],\n",
       "            \"roseType\": \"radius\",\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"labelLine\": {\n",
       "                \"show\": true,\n",
       "                \"showAbove\": false,\n",
       "                \"length\": 15,\n",
       "                \"length2\": 15,\n",
       "                \"smooth\": false,\n",
       "                \"minTurnAngle\": 90,\n",
       "                \"maxSurfaceAngle\": 90\n",
       "            }\n",
       "        },\n",
       "        {\n",
       "            \"type\": \"pie\",\n",
       "            \"colorBy\": \"data\",\n",
       "            \"legendHoverLink\": true,\n",
       "            \"selectedMode\": false,\n",
       "            \"selectedOffset\": 10,\n",
       "            \"clockwise\": true,\n",
       "            \"startAngle\": 90,\n",
       "            \"minAngle\": 0,\n",
       "            \"minShowLabelAngle\": 0,\n",
       "            \"avoidLabelOverlap\": true,\n",
       "            \"stillShowZeroSum\": true,\n",
       "            \"percentPrecision\": 2,\n",
       "            \"showEmptyCircle\": true,\n",
       "            \"emptyCircleStyle\": {\n",
       "                \"color\": \"lightgray\",\n",
       "                \"borderColor\": \"#000\",\n",
       "                \"borderWidth\": 0,\n",
       "                \"borderType\": \"solid\",\n",
       "                \"borderDashOffset\": 0,\n",
       "                \"borderCap\": \"butt\",\n",
       "                \"borderJoin\": \"bevel\",\n",
       "                \"borderMiterLimit\": 10,\n",
       "                \"opacity\": 1\n",
       "            },\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"\\u7855\\u58eb\",\n",
       "                    \"value\": 465\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u672c\\u79d1\",\n",
       "                    \"value\": 3900\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5927\\u4e13\",\n",
       "                    \"value\": 1666\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5b66\\u5386\\u4e0d\\u9650\",\n",
       "                    \"value\": 126\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9ad8\\u4e2d\",\n",
       "                    \"value\": 13\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u535a\\u58eb\",\n",
       "                    \"value\": 28\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e2d\\u4e13/\\u4e2d\\u6280\",\n",
       "                    \"value\": 19\n",
       "                }\n",
       "            ],\n",
       "            \"radius\": [\n",
       "                \"40%\",\n",
       "                \"55%\"\n",
       "            ],\n",
       "            \"center\": [\n",
       "                620,\n",
       "                220\n",
       "            ],\n",
       "            \"roseType\": \"area\",\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"labelLine\": {\n",
       "                \"show\": true,\n",
       "                \"showAbove\": false,\n",
       "                \"length\": 15,\n",
       "                \"length2\": 15,\n",
       "                \"smooth\": false,\n",
       "                \"minTurnAngle\": 90,\n",
       "                \"maxSurfaceAngle\": 90\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u7855\\u58eb\",\n",
       "                \"\\u672c\\u79d1\",\n",
       "                \"\\u5927\\u4e13\",\n",
       "                \"\\u5b66\\u5386\\u4e0d\\u9650\",\n",
       "                \"\\u9ad8\\u4e2d\",\n",
       "                \"\\u535a\\u58eb\",\n",
       "                \"\\u4e2d\\u4e13/\\u4e2d\\u6280\"\n",
       "            ],\n",
       "            \"selected\": {},\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14,\n",
       "            \"backgroundColor\": \"transparent\",\n",
       "            \"borderColor\": \"#ccc\",\n",
       "            \"borderWidth\": 1,\n",
       "            \"borderRadius\": 0,\n",
       "            \"pageButtonItemGap\": 5,\n",
       "            \"pageButtonPosition\": \"end\",\n",
       "            \"pageFormatter\": \"{current}/{total}\",\n",
       "            \"pageIconColor\": \"#2f4554\",\n",
       "            \"pageIconInactiveColor\": \"#aaa\",\n",
       "            \"pageIconSize\": 15,\n",
       "            \"animationDurationUpdate\": 800,\n",
       "            \"selector\": false,\n",
       "            \"selectorPosition\": \"auto\",\n",
       "            \"selectorItemGap\": 7,\n",
       "            \"selectorButtonGap\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"enterable\": false,\n",
       "        \"confine\": false,\n",
       "        \"appendToBody\": false,\n",
       "        \"transitionDuration\": 0.4,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5,\n",
       "        \"order\": \"seriesAsc\"\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"text\": \"\\u5404\\u5b66\\u5386\\u6570\\u91cf\\u5360\\u6bd4\",\n",
       "            \"target\": \"blank\",\n",
       "            \"subtarget\": \"blank\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"textAlign\": \"auto\",\n",
       "            \"textVerticalAlign\": \"auto\",\n",
       "            \"triggerEvent\": false\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_eb9a18055a814e38957733e57aed91e6.setOption(option_eb9a18055a814e38957733e57aed91e6);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x1e0c1a7e550>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num = [465,3900,1666,126,13,28,19]\n",
    "wd = ['硕士','本科','大专','学历不限','高中','博士','中专/中技']\n",
    "c =Pie()\n",
    "c.add(\"\",[list(z) for z in zip(wd,num)],radius = [\"40%\",\"55%\"],center = [240,220],rosetype='radius')\n",
    "c.add(\"\",[list(z) for z in zip(wd,num)],radius = [\"40%\",\"55%\"],center = [620,220],rosetype='area')\n",
    "c.set_global_opts(title_opts = opts.TitleOpts(title=\"各学历数量占比\"))\n",
    "c.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c5131898",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>职位名称</th>\n",
       "      <th>薪资范围</th>\n",
       "      <th>地点</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>学历要求</th>\n",
       "      <th>岗位标签</th>\n",
       "      <th>公司名称</th>\n",
       "      <th>公司类型</th>\n",
       "      <th>公司规模</th>\n",
       "      <th>省份</th>\n",
       "      <th>城市</th>\n",
       "      <th>薪资上限</th>\n",
       "      <th>薪资下限</th>\n",
       "      <th>处理后工作经验</th>\n",
       "      <th>薪资均值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>1万-2万</td>\n",
       "      <td>上海</td>\n",
       "      <td>不限</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['数据分析', '数据挖掘', '数据建模', 'SQL', 'Python']</td>\n",
       "      <td>好人生(上海)健康科技有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>1.2万-1.7万</td>\n",
       "      <td>上海-徐汇区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['Python', 'Tableau', '描述统计', 'R', 'Excel', 'P...</td>\n",
       "      <td>西安万兴服务外包有限责任公司</td>\n",
       "      <td>其它</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>7千-9千</td>\n",
       "      <td>上海</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['数据分析']</td>\n",
       "      <td>上海乔治白实业有限公司</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>2万-3万</td>\n",
       "      <td>上海-杨浦区</td>\n",
       "      <td>不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>['Python', 'SQL', '回归分析', '方差分析']</td>\n",
       "      <td>上海八客信息科技有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>1.4万-2万</td>\n",
       "      <td>上海-宝山区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['SQL', 'Python', 'Pandas', '决策树', 'SVM RNN', ...</td>\n",
       "      <td>成都奋途商贸有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>data scientist（数据分析）</td>\n",
       "      <td>1.9万-2.2万</td>\n",
       "      <td>上海-浦东新区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['Python']</td>\n",
       "      <td>宜通华瑞</td>\n",
       "      <td>民营</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>销售数据分析Contingent Business Support</td>\n",
       "      <td>1.5万-2万</td>\n",
       "      <td>上海-黄浦区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['Power BI', '制药/医疗', '数据分析']</td>\n",
       "      <td>科锐国际</td>\n",
       "      <td>合资</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Data Analysis数据分析专员</td>\n",
       "      <td>8千-1万</td>\n",
       "      <td>上海-长宁区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['数据分析', '销售业务数据']</td>\n",
       "      <td>上海柏迦科技有限公司</td>\n",
       "      <td>合资</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>数据分析助理经理(J12409)</td>\n",
       "      <td>1万-1.6万</td>\n",
       "      <td>上海-长宁区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['数据分析', '销售业务数据']</td>\n",
       "      <td>广州市斯凯奇商业有限公司</td>\n",
       "      <td>外商独资</td>\n",
       "      <td>500-999人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>13000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>数据分析JC</td>\n",
       "      <td>2.5万-5万</td>\n",
       "      <td>上海</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['数据分析', '数据集市', '需求分析', 'SQL']</td>\n",
       "      <td>中邮邮惠万家银行有限责任公司</td>\n",
       "      <td>国企</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                               职位名称       薪资范围       地点   工作经验  \\\n",
       "0           0                              数据分析师      1万-2万       上海     不限   \n",
       "1           1                              数据分析师  1.2万-1.7万   上海-徐汇区   1-3年   \n",
       "2           2                             数据分析专员      7千-9千       上海   1-3年   \n",
       "3           3                              数据分析师      2万-3万   上海-杨浦区     不限   \n",
       "4           4                              数据分析师    1.4万-2万   上海-宝山区   1-3年   \n",
       "5           5               data scientist（数据分析）  1.9万-2.2万  上海-浦东新区   1-3年   \n",
       "6           6  销售数据分析Contingent Business Support    1.5万-2万   上海-黄浦区   1-3年   \n",
       "7           7                Data Analysis数据分析专员      8千-1万   上海-长宁区   1-3年   \n",
       "8           8                   数据分析助理经理(J12409)    1万-1.6万   上海-长宁区  5-10年   \n",
       "9           9                             数据分析JC    2.5万-5万       上海   3-5年   \n",
       "\n",
       "  学历要求                                               岗位标签             公司名称  \\\n",
       "0   硕士          ['数据分析', '数据挖掘', '数据建模', 'SQL', 'Python']  好人生(上海)健康科技有限公司   \n",
       "1   本科  ['Python', 'Tableau', '描述统计', 'R', 'Excel', 'P...   西安万兴服务外包有限责任公司   \n",
       "2   本科                                           ['数据分析']      上海乔治白实业有限公司   \n",
       "3   本科                  ['Python', 'SQL', '回归分析', '方差分析']     上海八客信息科技有限公司   \n",
       "4   本科  ['SQL', 'Python', 'Pandas', '决策树', 'SVM RNN', ...       成都奋途商贸有限公司   \n",
       "5   本科                                         ['Python']             宜通华瑞   \n",
       "6   本科                      ['Power BI', '制药/医疗', '数据分析']             科锐国际   \n",
       "7   本科                                 ['数据分析', '销售业务数据']       上海柏迦科技有限公司   \n",
       "8   本科                                 ['数据分析', '销售业务数据']     广州市斯凯奇商业有限公司   \n",
       "9   本科                    ['数据分析', '数据集市', '需求分析', 'SQL']   中邮邮惠万家银行有限责任公司   \n",
       "\n",
       "    公司类型         公司规模  省份  城市     薪资上限     薪资下限  处理后工作经验     薪资均值  \n",
       "0    民营       20-99人   上海  上海  10000.0  20000.0      1.0  15000.0  \n",
       "1     其它      20-99人   上海  上海  12000.0  17000.0      2.0  14500.0  \n",
       "2  上市公司     100-299人   上海  上海   7000.0   9000.0      2.0   8000.0  \n",
       "3    民营   1000-9999人   上海  上海  20000.0  30000.0      1.0  25000.0  \n",
       "4    民营     100-299人   上海  上海  14000.0  20000.0      2.0  17000.0  \n",
       "5    民营   1000-9999人   上海  上海  19000.0  22000.0      2.0  20500.0  \n",
       "6    合资   1000-9999人   上海  上海  15000.0  20000.0      2.0  17500.0  \n",
       "7    合资     100-299人   上海  上海   8000.0  10000.0      2.0   9000.0  \n",
       "8  外商独资     500-999人   上海  上海  10000.0  16000.0      7.5  13000.0  \n",
       "9    国企     100-299人   上海  上海  25000.0  50000.0      4.0  37500.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beijing_demand = data2[data2['城市'] == '北京']['地点'].value_counts().sort_values(ascending = False)\n",
    "shanghai_demand =data2[data2['城市'] == '上海']['地点'].value_counts().sort_values(ascending = False)\n",
    "guangzhou_demand = data2[data2['城市'] == '广州']['地点'].value_counts().sort_values(ascending = False)\n",
    "shenzhen_demand = data2[data2['城市'] == '深圳']['地点'].value_counts().sort_values(ascending = False)\n",
    "data2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "56cb82f8",
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/v5/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"02f2e7360ca14ef6bb761f4be075809e\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_02f2e7360ca14ef6bb761f4be075809e = echarts.init(\n",
       "                    document.getElementById('02f2e7360ca14ef6bb761f4be075809e'), 'white', {renderer: 'canvas'});\n",
       "                var option_02f2e7360ca14ef6bb761f4be075809e = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"aria\": {\n",
       "        \"enabled\": false\n",
       "    },\n",
       "    \"color\": [\n",
       "        \"#5470c6\",\n",
       "        \"#91cc75\",\n",
       "        \"#fac858\",\n",
       "        \"#ee6666\",\n",
       "        \"#73c0de\",\n",
       "        \"#3ba272\",\n",
       "        \"#fc8452\",\n",
       "        \"#9a60b4\",\n",
       "        \"#ea7ccc\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"bar\",\n",
       "            \"name\": \"\\u5c97\\u4f4d\\u6570\",\n",
       "            \"legendHoverLink\": true,\n",
       "            \"data\": [\n",
       "                229,\n",
       "                197,\n",
       "                190,\n",
       "                68,\n",
       "                61,\n",
       "                48\n",
       "            ],\n",
       "            \"realtimeSort\": false,\n",
       "            \"showBackground\": false,\n",
       "            \"stackStrategy\": \"samesign\",\n",
       "            \"cursor\": \"pointer\",\n",
       "            \"barMinHeight\": 0,\n",
       "            \"barCategoryGap\": \"60%\",\n",
       "            \"barGap\": \"30%\",\n",
       "            \"large\": false,\n",
       "            \"largeThreshold\": 400,\n",
       "            \"seriesLayoutBy\": \"column\",\n",
       "            \"datasetIndex\": 0,\n",
       "            \"clip\": true,\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 2,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"itemStyle\": {\n",
       "                \"normal\": {\n",
       "                    \"barBorderRadius\": [\n",
       "                        30,\n",
       "                        30,\n",
       "                        30,\n",
       "                        30\n",
       "                    ],\n",
       "                    \"shadowColor\": \"rgb(0, 160, 221)\"\n",
       "                }\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u5c97\\u4f4d\\u6570\"\n",
       "            ],\n",
       "            \"selected\": {},\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14,\n",
       "            \"backgroundColor\": \"transparent\",\n",
       "            \"borderColor\": \"#ccc\",\n",
       "            \"borderWidth\": 1,\n",
       "            \"borderRadius\": 0,\n",
       "            \"pageButtonItemGap\": 5,\n",
       "            \"pageButtonPosition\": \"end\",\n",
       "            \"pageFormatter\": \"{current}/{total}\",\n",
       "            \"pageIconColor\": \"#2f4554\",\n",
       "            \"pageIconInactiveColor\": \"#aaa\",\n",
       "            \"pageIconSize\": 15,\n",
       "            \"animationDurationUpdate\": 800,\n",
       "            \"selector\": false,\n",
       "            \"selectorPosition\": \"auto\",\n",
       "            \"selectorItemGap\": 7,\n",
       "            \"selectorButtonGap\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"enterable\": false,\n",
       "        \"confine\": false,\n",
       "        \"appendToBody\": false,\n",
       "        \"transitionDuration\": 0.4,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5,\n",
       "        \"order\": \"seriesAsc\"\n",
       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"axisLabel\": {\n",
       "                \"rotate\": -45\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": true,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"\\u5317\\u4eac-\\u671d\\u9633\\u533a\",\n",
       "                \"\\u5317\\u4eac-\\u6d77\\u6dc0\\u533a\",\n",
       "                \"\\u5317\\u4eac\",\n",
       "                \"\\u5317\\u4eac-\\u4e1c\\u57ce\\u533a\",\n",
       "                \"\\u5317\\u4eac-\\u897f\\u57ce\\u533a\",\n",
       "                \"\\u5317\\u4eac-\\u5927\\u5174\\u533a\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": true,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"text\": \"\\u5317\\u4eac\\u5e02\\u5c97\\u4f4d\\u5206\\u5e03\\u6570\\u91cf\\u6392\\u884c\\uff08\\u524d6\\uff09\",\n",
       "            \"target\": \"blank\",\n",
       "            \"subtarget\": \"blank\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"textAlign\": \"auto\",\n",
       "            \"textVerticalAlign\": \"auto\",\n",
       "            \"triggerEvent\": false\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_02f2e7360ca14ef6bb761f4be075809e.setOption(option_02f2e7360ca14ef6bb761f4be075809e);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x1e0c1963210>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#绘制条形图-北京各区\n",
    "\n",
    "# 对数据进行排序\n",
    "sorted_data1 = beijing_demand.sort_values(ascending=False).head(6)\n",
    "\n",
    "c1 = (\n",
    "    Bar()\n",
    "    .add_xaxis(sorted_data1.index.tolist())\n",
    "    .add_yaxis(\"岗位数\", sorted_data1.values.tolist(), category_gap=\"60%\")\n",
    "    .set_series_opts(\n",
    "        itemstyle_opts={\n",
    "            \"normal\": {\n",
    "                \"barBorderRadius\": [30, 30, 30, 30],\n",
    "                \"shadowColor\": \"rgb(0, 160, 221)\",\n",
    "            }\n",
    "        }\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"北京市岗位分布数量排行（前6）\"),\n",
    "        xaxis_opts=opts.AxisOpts(axislabel_opts={\"rotate\": -45})\n",
    "    )\n",
    ")\n",
    "\n",
    "c1.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7b76f79a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/v5/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"b571b8e4eb3a4ff5a83e1eaa15173189\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_b571b8e4eb3a4ff5a83e1eaa15173189 = echarts.init(\n",
       "                    document.getElementById('b571b8e4eb3a4ff5a83e1eaa15173189'), 'white', {renderer: 'canvas'});\n",
       "                var option_b571b8e4eb3a4ff5a83e1eaa15173189 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"aria\": {\n",
       "        \"enabled\": false\n",
       "    },\n",
       "    \"color\": [\n",
       "        \"#5470c6\",\n",
       "        \"#91cc75\",\n",
       "        \"#fac858\",\n",
       "        \"#ee6666\",\n",
       "        \"#73c0de\",\n",
       "        \"#3ba272\",\n",
       "        \"#fc8452\",\n",
       "        \"#9a60b4\",\n",
       "        \"#ea7ccc\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"bar\",\n",
       "            \"name\": \"\\u5c97\\u4f4d\\u6570\",\n",
       "            \"legendHoverLink\": true,\n",
       "            \"data\": [\n",
       "                283,\n",
       "                150,\n",
       "                123,\n",
       "                87,\n",
       "                65,\n",
       "                56\n",
       "            ],\n",
       "            \"realtimeSort\": false,\n",
       "            \"showBackground\": false,\n",
       "            \"stackStrategy\": \"samesign\",\n",
       "            \"cursor\": \"pointer\",\n",
       "            \"barMinHeight\": 0,\n",
       "            \"barCategoryGap\": \"60%\",\n",
       "            \"barGap\": \"30%\",\n",
       "            \"large\": false,\n",
       "            \"largeThreshold\": 400,\n",
       "            \"seriesLayoutBy\": \"column\",\n",
       "            \"datasetIndex\": 0,\n",
       "            \"clip\": true,\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 2,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"itemStyle\": {\n",
       "                \"normal\": {\n",
       "                    \"barBorderRadius\": [\n",
       "                        30,\n",
       "                        30,\n",
       "                        30,\n",
       "                        30\n",
       "                    ],\n",
       "                    \"shadowColor\": \"rgb(0, 160, 221)\"\n",
       "                }\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u5c97\\u4f4d\\u6570\"\n",
       "            ],\n",
       "            \"selected\": {},\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14,\n",
       "            \"backgroundColor\": \"transparent\",\n",
       "            \"borderColor\": \"#ccc\",\n",
       "            \"borderWidth\": 1,\n",
       "            \"borderRadius\": 0,\n",
       "            \"pageButtonItemGap\": 5,\n",
       "            \"pageButtonPosition\": \"end\",\n",
       "            \"pageFormatter\": \"{current}/{total}\",\n",
       "            \"pageIconColor\": \"#2f4554\",\n",
       "            \"pageIconInactiveColor\": \"#aaa\",\n",
       "            \"pageIconSize\": 15,\n",
       "            \"animationDurationUpdate\": 800,\n",
       "            \"selector\": false,\n",
       "            \"selectorPosition\": \"auto\",\n",
       "            \"selectorItemGap\": 7,\n",
       "            \"selectorButtonGap\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"enterable\": false,\n",
       "        \"confine\": false,\n",
       "        \"appendToBody\": false,\n",
       "        \"transitionDuration\": 0.4,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5,\n",
       "        \"order\": \"seriesAsc\"\n",
       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"axisLabel\": {\n",
       "                \"rotate\": -45\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": true,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"\\u4e0a\\u6d77-\\u6d66\\u4e1c\\u65b0\\u533a\",\n",
       "                \"\\u4e0a\\u6d77\",\n",
       "                \"\\u4e0a\\u6d77-\\u5f90\\u6c47\\u533a\",\n",
       "                \"\\u4e0a\\u6d77-\\u9759\\u5b89\\u533a\",\n",
       "                \"\\u4e0a\\u6d77-\\u95f5\\u884c\\u533a\",\n",
       "                \"\\u4e0a\\u6d77-\\u9ec4\\u6d66\\u533a\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": true,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"text\": \"\\u4e0a\\u6d77\\u5e02\\u5c97\\u4f4d\\u5206\\u5e03\\u6570\\u91cf\\u6392\\u884c\\uff08\\u524d6\\uff09\",\n",
       "            \"target\": \"blank\",\n",
       "            \"subtarget\": \"blank\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"textAlign\": \"auto\",\n",
       "            \"textVerticalAlign\": \"auto\",\n",
       "            \"triggerEvent\": false\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_b571b8e4eb3a4ff5a83e1eaa15173189.setOption(option_b571b8e4eb3a4ff5a83e1eaa15173189);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x1e0c47a7950>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#绘制条形图-上海各区\n",
    "# 对数据进行排序并选择前6个条目\n",
    "sorted_data2 = shanghai_demand.sort_values(ascending=False).head(6)\n",
    "\n",
    "c2 = (\n",
    "    Bar()\n",
    "    .add_xaxis(sorted_data2.index.tolist())\n",
    "    .add_yaxis(\"岗位数\", sorted_data2.values.tolist(), category_gap=\"60%\")\n",
    "    .set_series_opts(\n",
    "        itemstyle_opts={\n",
    "            \"normal\": {\n",
    "                \"barBorderRadius\": [30, 30, 30, 30],\n",
    "                \"shadowColor\": \"rgb(0, 160, 221)\",\n",
    "            }\n",
    "        }\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"上海市岗位分布数量排行（前6）\"),\n",
    "        xaxis_opts=opts.AxisOpts(axislabel_opts={\"rotate\": -45})\n",
    "    )\n",
    ")\n",
    "\n",
    "c2.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1a37cfa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/v5/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"af2677156175471589c4ef7abcf3087f\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_af2677156175471589c4ef7abcf3087f = echarts.init(\n",
       "                    document.getElementById('af2677156175471589c4ef7abcf3087f'), 'white', {renderer: 'canvas'});\n",
       "                var option_af2677156175471589c4ef7abcf3087f = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"aria\": {\n",
       "        \"enabled\": false\n",
       "    },\n",
       "    \"color\": [\n",
       "        \"#5470c6\",\n",
       "        \"#91cc75\",\n",
       "        \"#fac858\",\n",
       "        \"#ee6666\",\n",
       "        \"#73c0de\",\n",
       "        \"#3ba272\",\n",
       "        \"#fc8452\",\n",
       "        \"#9a60b4\",\n",
       "        \"#ea7ccc\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"bar\",\n",
       "            \"name\": \"\\u5c97\\u4f4d\\u6570\",\n",
       "            \"legendHoverLink\": true,\n",
       "            \"data\": [\n",
       "                118,\n",
       "                55,\n",
       "                45,\n",
       "                44,\n",
       "                43,\n",
       "                20\n",
       "            ],\n",
       "            \"realtimeSort\": false,\n",
       "            \"showBackground\": false,\n",
       "            \"stackStrategy\": \"samesign\",\n",
       "            \"cursor\": \"pointer\",\n",
       "            \"barMinHeight\": 0,\n",
       "            \"barCategoryGap\": \"60%\",\n",
       "            \"barGap\": \"30%\",\n",
       "            \"large\": false,\n",
       "            \"largeThreshold\": 400,\n",
       "            \"seriesLayoutBy\": \"column\",\n",
       "            \"datasetIndex\": 0,\n",
       "            \"clip\": true,\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 2,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"itemStyle\": {\n",
       "                \"normal\": {\n",
       "                    \"barBorderRadius\": [\n",
       "                        30,\n",
       "                        30,\n",
       "                        30,\n",
       "                        30\n",
       "                    ],\n",
       "                    \"shadowColor\": \"rgb(0, 160, 221)\"\n",
       "                }\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u5c97\\u4f4d\\u6570\"\n",
       "            ],\n",
       "            \"selected\": {},\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14,\n",
       "            \"backgroundColor\": \"transparent\",\n",
       "            \"borderColor\": \"#ccc\",\n",
       "            \"borderWidth\": 1,\n",
       "            \"borderRadius\": 0,\n",
       "            \"pageButtonItemGap\": 5,\n",
       "            \"pageButtonPosition\": \"end\",\n",
       "            \"pageFormatter\": \"{current}/{total}\",\n",
       "            \"pageIconColor\": \"#2f4554\",\n",
       "            \"pageIconInactiveColor\": \"#aaa\",\n",
       "            \"pageIconSize\": 15,\n",
       "            \"animationDurationUpdate\": 800,\n",
       "            \"selector\": false,\n",
       "            \"selectorPosition\": \"auto\",\n",
       "            \"selectorItemGap\": 7,\n",
       "            \"selectorButtonGap\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"enterable\": false,\n",
       "        \"confine\": false,\n",
       "        \"appendToBody\": false,\n",
       "        \"transitionDuration\": 0.4,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5,\n",
       "        \"order\": \"seriesAsc\"\n",
       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"axisLabel\": {\n",
       "                \"rotate\": -45\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": true,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"\\u5e7f\\u5dde-\\u5929\\u6cb3\\u533a\",\n",
       "                \"\\u5e7f\\u5dde\",\n",
       "                \"\\u5e7f\\u5dde-\\u8d8a\\u79c0\\u533a\",\n",
       "                \"\\u5e7f\\u5dde-\\u6d77\\u73e0\\u533a\",\n",
       "                \"\\u5e7f\\u5dde-\\u9ec4\\u57d4\\u533a\",\n",
       "                \"\\u5e7f\\u5dde-\\u767d\\u4e91\\u533a\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": true,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"text\": \"\\u5e7f\\u5dde\\u5e02\\u5c97\\u4f4d\\u5206\\u5e03\\u6570\\u91cf\\u6392\\u884c\\uff08\\u524d6\\uff09\",\n",
       "            \"target\": \"blank\",\n",
       "            \"subtarget\": \"blank\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"textAlign\": \"auto\",\n",
       "            \"textVerticalAlign\": \"auto\",\n",
       "            \"triggerEvent\": false\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_af2677156175471589c4ef7abcf3087f.setOption(option_af2677156175471589c4ef7abcf3087f);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x1e0c480c190>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#绘制条形图-广州各区\n",
    "# 对数据进行排序并选择前6个条目\n",
    "sorted_data3 = guangzhou_demand.sort_values(ascending=False).head(6)\n",
    "\n",
    "c3 = (\n",
    "    Bar()\n",
    "    .add_xaxis(sorted_data3.index.tolist())\n",
    "    .add_yaxis(\"岗位数\", sorted_data3.values.tolist(), category_gap=\"60%\")\n",
    "    .set_series_opts(\n",
    "        itemstyle_opts={\n",
    "            \"normal\": {\n",
    "                \"barBorderRadius\": [30, 30, 30, 30],\n",
    "                \"shadowColor\": \"rgb(0, 160, 221)\",\n",
    "            }\n",
    "        }\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"广州市岗位分布数量排行（前6）\"),\n",
    "        xaxis_opts=opts.AxisOpts(axislabel_opts={\"rotate\": -45})\n",
    "    )\n",
    ")\n",
    "\n",
    "c3.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "93296fd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/v5/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"e844d31f7c1342fda61f7b44bf487ee1\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_e844d31f7c1342fda61f7b44bf487ee1 = echarts.init(\n",
       "                    document.getElementById('e844d31f7c1342fda61f7b44bf487ee1'), 'white', {renderer: 'canvas'});\n",
       "                var option_e844d31f7c1342fda61f7b44bf487ee1 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"aria\": {\n",
       "        \"enabled\": false\n",
       "    },\n",
       "    \"color\": [\n",
       "        \"#5470c6\",\n",
       "        \"#91cc75\",\n",
       "        \"#fac858\",\n",
       "        \"#ee6666\",\n",
       "        \"#73c0de\",\n",
       "        \"#3ba272\",\n",
       "        \"#fc8452\",\n",
       "        \"#9a60b4\",\n",
       "        \"#ea7ccc\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"bar\",\n",
       "            \"name\": \"\\u5c97\\u4f4d\\u6570\",\n",
       "            \"legendHoverLink\": true,\n",
       "            \"data\": [\n",
       "                163,\n",
       "                130,\n",
       "                102,\n",
       "                93,\n",
       "                61,\n",
       "                39\n",
       "            ],\n",
       "            \"realtimeSort\": false,\n",
       "            \"showBackground\": false,\n",
       "            \"stackStrategy\": \"samesign\",\n",
       "            \"cursor\": \"pointer\",\n",
       "            \"barMinHeight\": 0,\n",
       "            \"barCategoryGap\": \"60%\",\n",
       "            \"barGap\": \"30%\",\n",
       "            \"large\": false,\n",
       "            \"largeThreshold\": 400,\n",
       "            \"seriesLayoutBy\": \"column\",\n",
       "            \"datasetIndex\": 0,\n",
       "            \"clip\": true,\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 2,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"itemStyle\": {\n",
       "                \"normal\": {\n",
       "                    \"barBorderRadius\": [\n",
       "                        30,\n",
       "                        30,\n",
       "                        30,\n",
       "                        30\n",
       "                    ],\n",
       "                    \"shadowColor\": \"rgb(0, 160, 221)\"\n",
       "                }\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u5c97\\u4f4d\\u6570\"\n",
       "            ],\n",
       "            \"selected\": {},\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14,\n",
       "            \"backgroundColor\": \"transparent\",\n",
       "            \"borderColor\": \"#ccc\",\n",
       "            \"borderWidth\": 1,\n",
       "            \"borderRadius\": 0,\n",
       "            \"pageButtonItemGap\": 5,\n",
       "            \"pageButtonPosition\": \"end\",\n",
       "            \"pageFormatter\": \"{current}/{total}\",\n",
       "            \"pageIconColor\": \"#2f4554\",\n",
       "            \"pageIconInactiveColor\": \"#aaa\",\n",
       "            \"pageIconSize\": 15,\n",
       "            \"animationDurationUpdate\": 800,\n",
       "            \"selector\": false,\n",
       "            \"selectorPosition\": \"auto\",\n",
       "            \"selectorItemGap\": 7,\n",
       "            \"selectorButtonGap\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"enterable\": false,\n",
       "        \"confine\": false,\n",
       "        \"appendToBody\": false,\n",
       "        \"transitionDuration\": 0.4,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5,\n",
       "        \"order\": \"seriesAsc\"\n",
       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"axisLabel\": {\n",
       "                \"rotate\": -45\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": true,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"\\u6df1\\u5733-\\u5357\\u5c71\\u533a\",\n",
       "                \"\\u6df1\\u5733-\\u798f\\u7530\\u533a\",\n",
       "                \"\\u6df1\\u5733\",\n",
       "                \"\\u6df1\\u5733-\\u9f99\\u5c97\\u533a\",\n",
       "                \"\\u6df1\\u5733-\\u5b9d\\u5b89\\u533a\",\n",
       "                \"\\u6df1\\u5733-\\u9f99\\u534e\\u533a\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": true,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"text\": \"\\u6df1\\u5733\\u5e02\\u5c97\\u4f4d\\u5206\\u5e03\\u6570\\u91cf\\u6392\\u884c\\uff08\\u524d6\\uff09\",\n",
       "            \"target\": \"blank\",\n",
       "            \"subtarget\": \"blank\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"textAlign\": \"auto\",\n",
       "            \"textVerticalAlign\": \"auto\",\n",
       "            \"triggerEvent\": false\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_e844d31f7c1342fda61f7b44bf487ee1.setOption(option_e844d31f7c1342fda61f7b44bf487ee1);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x1e0c480d410>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#绘制条形图-深圳各区\n",
    "# 对数据进行排序并选择前6个条目\n",
    "sorted_data4 = shenzhen_demand.sort_values(ascending=False).head(6)\n",
    "\n",
    "c4 = (\n",
    "    Bar()\n",
    "    .add_xaxis(sorted_data4.index.tolist())\n",
    "    .add_yaxis(\"岗位数\", sorted_data4.values.tolist(), category_gap=\"60%\")\n",
    "    .set_series_opts(\n",
    "        itemstyle_opts={\n",
    "            \"normal\": {\n",
    "                \"barBorderRadius\": [30, 30, 30, 30],\n",
    "                \"shadowColor\": \"rgb(0, 160, 221)\",\n",
    "            }\n",
    "        }\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"深圳市岗位分布数量排行（前6）\"),\n",
    "        xaxis_opts=opts.AxisOpts(axislabel_opts={\"rotate\": -45})\n",
    "    )\n",
    ")\n",
    "\n",
    "c4.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "74ed9612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))  # 设置图表大小\n",
    "sns.boxplot(x='工作经验', y='薪资均值', data=data2)\n",
    "plt.ylim(1500, 100000)  # 设置 Y 轴范围\n",
    "plt.title('工作经验与薪资均值的关系')  # 设置图表标题\n",
    "plt.xlabel('工作经验')  # 设置 x 轴标签\n",
    "plt.ylabel('薪资均值')  # 设置 y 轴标签\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4ae9fbf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))  # 设置图表大小\n",
    "sns.violinplot(x='工作经验', y='薪资均值', data=data2)\n",
    "# plt.figure(figsize=(8, 8))  # 设置图表大小\n",
    "plt.ylim(1500, 100000)  # 设置 Y 轴范围\n",
    "plt.title('工作经验与薪资均值的关系')  # 设置图表标题\n",
    "plt.xlabel('工作经验')  # 设置 x 轴标签\n",
    "plt.ylabel('薪资均值')  # 设置 y 轴标签\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7febdea4",
   "metadata": {},
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>职位名称</th>\n",
       "      <th>薪资范围</th>\n",
       "      <th>地点</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>学历要求</th>\n",
       "      <th>岗位标签</th>\n",
       "      <th>公司名称</th>\n",
       "      <th>公司类型</th>\n",
       "      <th>公司规模</th>\n",
       "      <th>省份</th>\n",
       "      <th>城市</th>\n",
       "      <th>薪资上限</th>\n",
       "      <th>薪资下限</th>\n",
       "      <th>处理后工作经验</th>\n",
       "      <th>薪资均值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>2030</td>\n",
       "      <td>数据治理工程师</td>\n",
       "      <td>12万-20万</td>\n",
       "      <td>南京</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['数据治理', '大数据开发', 'Java', 'SQL']</td>\n",
       "      <td>江苏金恒信息科技股份有限公司</td>\n",
       "      <td>股份制企业</td>\n",
       "      <td>500-999人</td>\n",
       "      <td>江苏</td>\n",
       "      <td>南京</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>160000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4721</th>\n",
       "      <td>4721</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>10万-20万</td>\n",
       "      <td>深圳-南山区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['数据分析', 'Python', '爬虫', '数据清洗', 'Java']</td>\n",
       "      <td>江西省众灿互动科技股份有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>广东</td>\n",
       "      <td>深圳</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>361</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>8万-10万</td>\n",
       "      <td>上海</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['SQL']</td>\n",
       "      <td>上海利真</td>\n",
       "      <td>民营</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>90000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4626</th>\n",
       "      <td>4626</td>\n",
       "      <td>数据分析架构高端专家</td>\n",
       "      <td>6万-10万</td>\n",
       "      <td>上海-浦东新区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['数据分析', '数据架构', 'Hadoop', 'Spark']</td>\n",
       "      <td>南京蓝魔云信息技术有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>广东</td>\n",
       "      <td>深圳</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>80000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>822</td>\n",
       "      <td>统计副总监</td>\n",
       "      <td>6万-9万</td>\n",
       "      <td>上海-徐汇区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['生物工程', '临床试验', '医药科研']</td>\n",
       "      <td>成都方向标企业管理咨询有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>75000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>487</td>\n",
       "      <td>分析经理</td>\n",
       "      <td>6万-8万</td>\n",
       "      <td>上海-青浦区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['相关分析', '因子分析', '多元统计分析']</td>\n",
       "      <td>银科控股</td>\n",
       "      <td>外商独资</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>70000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3240</th>\n",
       "      <td>3240</td>\n",
       "      <td>商业分析专家</td>\n",
       "      <td>5万-8万</td>\n",
       "      <td>成都-锦江区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['SQL', 'Tableau', '教育培训', '数学/统计学专业', '管理咨询工作...</td>\n",
       "      <td>北京仕邦达人力资源服务有限公司上海分公司</td>\n",
       "      <td>合资</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>四川</td>\n",
       "      <td>成都</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>65000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2883</th>\n",
       "      <td>2883</td>\n",
       "      <td>大数据团队总监</td>\n",
       "      <td>4万-8万</td>\n",
       "      <td>广州-越秀区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['数据分析', '大数据分析', '数据处理', '数据清洗', '数据架构', '数据库...</td>\n",
       "      <td>广东省国际工程咨询有限公司</td>\n",
       "      <td>国企</td>\n",
       "      <td>500-999人</td>\n",
       "      <td>广东</td>\n",
       "      <td>广州</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>60000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1646</th>\n",
       "      <td>1646</td>\n",
       "      <td>数据治理专家</td>\n",
       "      <td>4万-8万</td>\n",
       "      <td>北京-东城区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['Hadoop', 'Spark', 'Kafka', 'Flink', '数据治理', ...</td>\n",
       "      <td>北京睿和良木管理咨询有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>500-999人</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>60000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3686</th>\n",
       "      <td>3686</td>\n",
       "      <td>数据部门负责人</td>\n",
       "      <td>4万-8万</td>\n",
       "      <td>杭州-拱墅区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['生物统计学', 'CRF设计', '生物医疗', '药物研究']</td>\n",
       "      <td>深圳今日人才信息科技有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>浙江</td>\n",
       "      <td>杭州</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>60000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>381</td>\n",
       "      <td>数据分析总监</td>\n",
       "      <td>4.5万-7万</td>\n",
       "      <td>上海-黄浦区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['数据分析', 'SQL', '数据治理', '数据挖掘', '数据清洗', '数据仓库'...</td>\n",
       "      <td>上海奥深商务咨询有限公司</td>\n",
       "      <td>外商独资</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>57500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1381</th>\n",
       "      <td>1381</td>\n",
       "      <td>数据科学家</td>\n",
       "      <td>4.5万-7万</td>\n",
       "      <td>北京-海淀区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['SQL', '数据分析', '团队管理']</td>\n",
       "      <td>北京嘀嘀无限科技发展有限公司</td>\n",
       "      <td>外商独资</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>57500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>595</td>\n",
       "      <td>医学总监</td>\n",
       "      <td>4万-7万</td>\n",
       "      <td>上海-浦东新区</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['医学', '方案撰写', '摘要']</td>\n",
       "      <td>北京上予医药科技有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>55000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>224</td>\n",
       "      <td>业务分析负责人</td>\n",
       "      <td>4万-7万</td>\n",
       "      <td>上海-长宁区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['SQL', 'Power-BI', '平台类产品']</td>\n",
       "      <td>满帮集团</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>55000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>437</td>\n",
       "      <td>高级数据分析师（北京/上海）</td>\n",
       "      <td>4万-7万</td>\n",
       "      <td>上海-黄浦区</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['SQL']</td>\n",
       "      <td>华世先达咨询(北京)有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>55000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>853</td>\n",
       "      <td>Associate Director, Biostatistics (MJ000030)</td>\n",
       "      <td>4万-7万</td>\n",
       "      <td>上海-徐汇区</td>\n",
       "      <td>10年以上</td>\n",
       "      <td>博士</td>\n",
       "      <td>['统计']</td>\n",
       "      <td>和铂医药</td>\n",
       "      <td>港澳台公司</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>55000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>1456</td>\n",
       "      <td>商业分析总监(MJ003717)</td>\n",
       "      <td>4万-7万</td>\n",
       "      <td>北京</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['商业分析', '策略运营', '行业分析', '战略研究', '数据分析']</td>\n",
       "      <td>北京健康之家科技有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>55000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4834</th>\n",
       "      <td>4834</td>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>3.5万-7万</td>\n",
       "      <td>深圳-福田区</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['SQL', 'Python', '数据分析', 'R']</td>\n",
       "      <td>华世先达咨询(北京)有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>广东</td>\n",
       "      <td>深圳</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>52500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5821</th>\n",
       "      <td>5821</td>\n",
       "      <td>大数据部部长</td>\n",
       "      <td>3.5万-7万</td>\n",
       "      <td>重庆-沙坪坝区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['大数据、车联网']</td>\n",
       "      <td>深圳今日人才信息科技有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>重庆</td>\n",
       "      <td>重庆</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>52500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1763</th>\n",
       "      <td>1763</td>\n",
       "      <td>临床数据管理经理</td>\n",
       "      <td>3.5万-7万</td>\n",
       "      <td>北京-东城区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['CRF设计', '生物统计学', '医学', '临床数据分析', '临床试验', '临床...</td>\n",
       "      <td>麦科奥特</td>\n",
       "      <td>合资</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5104</th>\n",
       "      <td>5104</td>\n",
       "      <td>高级统计师</td>\n",
       "      <td>4万-6万</td>\n",
       "      <td>福州-鼓楼区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['临床数据分析', 'SAS', '生物统计']</td>\n",
       "      <td>全益(上海)人力资源有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>福建</td>\n",
       "      <td>福州</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5234</th>\n",
       "      <td>5234</td>\n",
       "      <td>高级统计师</td>\n",
       "      <td>4万-6万</td>\n",
       "      <td>苏州-吴中区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['临床数据分析', 'SAS', '生物统计']</td>\n",
       "      <td>全益(上海)人力资源有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>江苏</td>\n",
       "      <td>苏州</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2978</th>\n",
       "      <td>2978</td>\n",
       "      <td>高级生物统计师</td>\n",
       "      <td>4万-6万</td>\n",
       "      <td>广州-越秀区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['SAS', 'SAP', 'SAR', '临床试验方案设计', '生物统计师', 'ED...</td>\n",
       "      <td>深圳高邦信息技术有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>广东</td>\n",
       "      <td>广州</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5874</th>\n",
       "      <td>5875</td>\n",
       "      <td>高级统计师</td>\n",
       "      <td>4万-6万</td>\n",
       "      <td>长春-宽城区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['临床数据分析', 'SAS', '生物统计']</td>\n",
       "      <td>全益(上海)人力资源有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>吉林</td>\n",
       "      <td>长春</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5658</th>\n",
       "      <td>5658</td>\n",
       "      <td>高级统计师</td>\n",
       "      <td>4万-6万</td>\n",
       "      <td>郑州-中原区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['临床数据分析', 'SAS', '生物统计']</td>\n",
       "      <td>全益(上海)人力资源有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>河南</td>\n",
       "      <td>郑州</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>387</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>4万-6万</td>\n",
       "      <td>上海-徐汇区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['Python', 'SQL', '回归分析', '聚类分析']</td>\n",
       "      <td>湖北斯克斯人力资源有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>300-499人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4887</th>\n",
       "      <td>4887</td>\n",
       "      <td>高级统计师</td>\n",
       "      <td>4万-6万</td>\n",
       "      <td>深圳-龙华区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['临床数据分析', 'SAS', '生物统计']</td>\n",
       "      <td>全益(上海)人力资源有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>广东</td>\n",
       "      <td>深圳</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1806</th>\n",
       "      <td>1806</td>\n",
       "      <td>数据总监</td>\n",
       "      <td>4万-6万</td>\n",
       "      <td>北京-通州区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['多元统计分析']</td>\n",
       "      <td>北京联东投资（集团）有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4880</th>\n",
       "      <td>4880</td>\n",
       "      <td>高级统计师</td>\n",
       "      <td>4万-6万</td>\n",
       "      <td>深圳-南山区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['临床数据分析', 'SAS', '生物统计']</td>\n",
       "      <td>全益(上海)人力资源有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>广东</td>\n",
       "      <td>深圳</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6067</th>\n",
       "      <td>6068</td>\n",
       "      <td>高级生物统计师</td>\n",
       "      <td>4万-6万</td>\n",
       "      <td>长沙-开福区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['SAS', 'SAP', 'SAR', '临床试验方案设计', '生物统计师', 'ED...</td>\n",
       "      <td>深圳高邦信息技术有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>湖南</td>\n",
       "      <td>长沙</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6262</th>\n",
       "      <td>6263</td>\n",
       "      <td>高级统计师</td>\n",
       "      <td>4万-6万</td>\n",
       "      <td>青岛-市南区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['临床数据分析', 'SAS', '生物统计']</td>\n",
       "      <td>全益(上海)人力资源有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>山东</td>\n",
       "      <td>青岛</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>797</td>\n",
       "      <td>商情总监/研投总监</td>\n",
       "      <td>4万-6万</td>\n",
       "      <td>上海-黄浦区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['行业研究', '市场研究', '市场信息收集']</td>\n",
       "      <td>北京大北农贸易有限责任公司</td>\n",
       "      <td></td>\n",
       "      <td>20人以下</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>531</td>\n",
       "      <td>量化研究员</td>\n",
       "      <td>4万-6万</td>\n",
       "      <td>上海-徐汇区</td>\n",
       "      <td>无经验</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['物理、数学', '量化分析', 'Python', '股票市场', '基金市场']</td>\n",
       "      <td>蓝泉(徐州)信息科技有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3992</th>\n",
       "      <td>3992</td>\n",
       "      <td>高级统计师</td>\n",
       "      <td>4万-6万</td>\n",
       "      <td>武汉-江汉区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['临床数据分析', 'SAS', '生物统计']</td>\n",
       "      <td>全益(上海)人力资源有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>湖北</td>\n",
       "      <td>武汉</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>561</td>\n",
       "      <td>高级统计师</td>\n",
       "      <td>4万-6万</td>\n",
       "      <td>上海-长宁区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['临床数据分析', 'SAS', '生物统计']</td>\n",
       "      <td>全益(上海)人力资源有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2094</th>\n",
       "      <td>2094</td>\n",
       "      <td>高级统计师</td>\n",
       "      <td>4万-6万</td>\n",
       "      <td>南京-玄武区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['临床数据分析', 'SAS', '生物统计']</td>\n",
       "      <td>全益(上海)人力资源有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>江苏</td>\n",
       "      <td>南京</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2089</th>\n",
       "      <td>2089</td>\n",
       "      <td>高级统计师</td>\n",
       "      <td>4万-6万</td>\n",
       "      <td>南京-鼓楼区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['临床数据分析', 'SAS', '生物统计']</td>\n",
       "      <td>全益(上海)人力资源有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>江苏</td>\n",
       "      <td>南京</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>753</td>\n",
       "      <td>量化策略研究员（中高频/低频Alpha）</td>\n",
       "      <td>4万-6万</td>\n",
       "      <td>上海-浦东新区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['期货研究', '证券研究']</td>\n",
       "      <td>深圳市昱辰宽客管理咨询有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20人以下</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1526</th>\n",
       "      <td>1526</td>\n",
       "      <td>经营分析规划</td>\n",
       "      <td>3.5万-6万</td>\n",
       "      <td>北京-海淀区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['SQL']</td>\n",
       "      <td>北京福睿思特国际人力资源有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>47500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>194</td>\n",
       "      <td>大数据分析师</td>\n",
       "      <td>3万-6万</td>\n",
       "      <td>上海-浦东新区</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['Python', 'SQL', 'Spark', 'MapReduce', 'Hadoo...</td>\n",
       "      <td>中邮邮惠万家银行有限责任公司</td>\n",
       "      <td>国企</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>45000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1602</th>\n",
       "      <td>1602</td>\n",
       "      <td>华泰证券-信息技术部-资深数据工程师</td>\n",
       "      <td>3万-6万</td>\n",
       "      <td>北京-西城区</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['SQL', 'Hadoop']</td>\n",
       "      <td>亨特爾獵頭(中國)公司</td>\n",
       "      <td>外商独资</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>45000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1840</th>\n",
       "      <td>1840</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>3万-6万</td>\n",
       "      <td>北京-朝阳区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['SQL', '线下零售', '生活服务/O2O']</td>\n",
       "      <td>卓一嘉越管理咨询(连云港)有限公司</td>\n",
       "      <td>股份制企业</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>45000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>218</td>\n",
       "      <td>中银证券--数据开发工程师</td>\n",
       "      <td>3万-6万</td>\n",
       "      <td>上海-浦东新区</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['Python', 'SQL']</td>\n",
       "      <td>亨特爾獵頭(中國)公司</td>\n",
       "      <td>外商独资</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>45000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2992</th>\n",
       "      <td>2992</td>\n",
       "      <td>高级统计师</td>\n",
       "      <td>3万-6万</td>\n",
       "      <td>广州-越秀区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['统计分析', '临床数据分析']</td>\n",
       "      <td>联邦制药</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>广东</td>\n",
       "      <td>广州</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>45000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>255</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>3万-6万</td>\n",
       "      <td>上海</td>\n",
       "      <td>不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>['商业数据分析', '电商数据分析', '游戏数据分析', 'Hive']</td>\n",
       "      <td>拼多多</td>\n",
       "      <td>民营</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>667</td>\n",
       "      <td>金融量化工程师</td>\n",
       "      <td>3万-6万</td>\n",
       "      <td>上海-徐汇区</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['金融', '量化分析', '证券从业资格证', '基金从业资格证', '证券数据分析',...</td>\n",
       "      <td>蓝泉(徐州)信息科技有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>100-299人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>45000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4760</th>\n",
       "      <td>4760</td>\n",
       "      <td>数据分析经理（小微企业）</td>\n",
       "      <td>3万-6万</td>\n",
       "      <td>深圳-南山区</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>['数据分析', 'SQL', 'Python']</td>\n",
       "      <td>展动力</td>\n",
       "      <td>民营</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>广东</td>\n",
       "      <td>深圳</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>45000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>608</td>\n",
       "      <td>数据BI分析师</td>\n",
       "      <td>3.5万-5.5万</td>\n",
       "      <td>上海-长宁区</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['SQL', 'Hive', 'Python', 'R']</td>\n",
       "      <td>安拓奥古(北京)人力资源服务有限公司</td>\n",
       "      <td>合资</td>\n",
       "      <td>300-499人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>45000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6015</th>\n",
       "      <td>6016</td>\n",
       "      <td>数据分析总监</td>\n",
       "      <td>4万-5万</td>\n",
       "      <td>长沙-望城区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>['数据分析', '商业数据分析', '运营数据分析', '市场数据分析', '经营数据分析...</td>\n",
       "      <td>西双版纳创优餐饮管理有限公司</td>\n",
       "      <td>民营</td>\n",
       "      <td>1000-9999人</td>\n",
       "      <td>湖南</td>\n",
       "      <td>长沙</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>45000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>512</td>\n",
       "      <td>生物统计/编程/数据管理等</td>\n",
       "      <td>3万-6万</td>\n",
       "      <td>上海-徐汇区</td>\n",
       "      <td>不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>['生物统计学', '生物医疗', 'SQL', 'SAS', '数据管理']</td>\n",
       "      <td>上海沛生企业管理中心</td>\n",
       "      <td>合资</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                                          职位名称       薪资范围  \\\n",
       "2030        2030                                       数据治理工程师    12万-20万   \n",
       "4721        4721                                         数据分析师    10万-20万   \n",
       "361          361                                         数据分析师     8万-10万   \n",
       "4626        4626                                    数据分析架构高端专家     6万-10万   \n",
       "822          822                                         统计副总监      6万-9万   \n",
       "487          487                                          分析经理      6万-8万   \n",
       "3240        3240                                        商业分析专家      5万-8万   \n",
       "2883        2883                                       大数据团队总监      4万-8万   \n",
       "1646        1646                                        数据治理专家      4万-8万   \n",
       "3686        3686                                       数据部门负责人      4万-8万   \n",
       "381          381                                        数据分析总监    4.5万-7万   \n",
       "1381        1381                                         数据科学家    4.5万-7万   \n",
       "595          595                                          医学总监      4万-7万   \n",
       "224          224                                       业务分析负责人      4万-7万   \n",
       "437          437                                高级数据分析师（北京/上海）      4万-7万   \n",
       "853          853  Associate Director, Biostatistics (MJ000030)      4万-7万   \n",
       "1456        1456                              商业分析总监(MJ003717)      4万-7万   \n",
       "4834        4834                                       高级数据分析师    3.5万-7万   \n",
       "5821        5821                                        大数据部部长    3.5万-7万   \n",
       "1763        1763                                      临床数据管理经理    3.5万-7万   \n",
       "5104        5104                                         高级统计师      4万-6万   \n",
       "5234        5234                                         高级统计师      4万-6万   \n",
       "2978        2978                                       高级生物统计师      4万-6万   \n",
       "5874        5875                                         高级统计师      4万-6万   \n",
       "5658        5658                                         高级统计师      4万-6万   \n",
       "387          387                                         数据分析师      4万-6万   \n",
       "4887        4887                                         高级统计师      4万-6万   \n",
       "1806        1806                                          数据总监      4万-6万   \n",
       "4880        4880                                         高级统计师      4万-6万   \n",
       "6067        6068                                       高级生物统计师      4万-6万   \n",
       "6262        6263                                         高级统计师      4万-6万   \n",
       "797          797                                     商情总监/研投总监      4万-6万   \n",
       "531          531                                         量化研究员      4万-6万   \n",
       "3992        3992                                         高级统计师      4万-6万   \n",
       "561          561                                         高级统计师      4万-6万   \n",
       "2094        2094                                         高级统计师      4万-6万   \n",
       "2089        2089                                         高级统计师      4万-6万   \n",
       "753          753                          量化策略研究员（中高频/低频Alpha）      4万-6万   \n",
       "1526        1526                                        经营分析规划    3.5万-6万   \n",
       "194          194                                        大数据分析师      3万-6万   \n",
       "1602        1602                            华泰证券-信息技术部-资深数据工程师      3万-6万   \n",
       "1840        1840                                         数据分析师      3万-6万   \n",
       "218          218                                 中银证券--数据开发工程师      3万-6万   \n",
       "2992        2992                                         高级统计师      3万-6万   \n",
       "255          255                                         数据分析师      3万-6万   \n",
       "667          667                                       金融量化工程师      3万-6万   \n",
       "4760        4760                                  数据分析经理（小微企业）      3万-6万   \n",
       "608          608                                       数据BI分析师  3.5万-5.5万   \n",
       "6015        6016                                        数据分析总监      4万-5万   \n",
       "512          512                                 生物统计/编程/数据管理等      3万-6万   \n",
       "\n",
       "           地点   工作经验 学历要求                                               岗位标签  \\\n",
       "2030       南京   3-5年   本科                   ['数据治理', '大数据开发', 'Java', 'SQL']   \n",
       "4721   深圳-南山区   1-3年   本科           ['数据分析', 'Python', '爬虫', '数据清洗', 'Java']   \n",
       "361        上海   1-3年   本科                                            ['SQL']   \n",
       "4626  上海-浦东新区  5-10年   本科                ['数据分析', '数据架构', 'Hadoop', 'Spark']   \n",
       "822    上海-徐汇区  5-10年   硕士                           ['生物工程', '临床试验', '医药科研']   \n",
       "487    上海-青浦区  5-10年   本科                         ['相关分析', '因子分析', '多元统计分析']   \n",
       "3240   成都-锦江区  5-10年   本科  ['SQL', 'Tableau', '教育培训', '数学/统计学专业', '管理咨询工作...   \n",
       "2883   广州-越秀区  5-10年   本科  ['数据分析', '大数据分析', '数据处理', '数据清洗', '数据架构', '数据库...   \n",
       "1646   北京-东城区  5-10年   本科  ['Hadoop', 'Spark', 'Kafka', 'Flink', '数据治理', ...   \n",
       "3686   杭州-拱墅区  5-10年   本科                 ['生物统计学', 'CRF设计', '生物医疗', '药物研究']   \n",
       "381    上海-黄浦区  5-10年   本科  ['数据分析', 'SQL', '数据治理', '数据挖掘', '数据清洗', '数据仓库'...   \n",
       "1381   北京-海淀区  5-10年   本科                            ['SQL', '数据分析', '团队管理']   \n",
       "595   上海-浦东新区   3-5年   硕士                               ['医学', '方案撰写', '摘要']   \n",
       "224    上海-长宁区  5-10年   本科                       ['SQL', 'Power-BI', '平台类产品']   \n",
       "437    上海-黄浦区   3-5年   硕士                                            ['SQL']   \n",
       "853    上海-徐汇区  10年以上   博士                                             ['统计']   \n",
       "1456       北京  5-10年   本科           ['商业分析', '策略运营', '行业分析', '战略研究', '数据分析']   \n",
       "4834   深圳-福田区   3-5年   本科                     ['SQL', 'Python', '数据分析', 'R']   \n",
       "5821  重庆-沙坪坝区  5-10年   本科                                        ['大数据、车联网']   \n",
       "1763   北京-东城区   1-3年   硕士  ['CRF设计', '生物统计学', '医学', '临床数据分析', '临床试验', '临床...   \n",
       "5104   福州-鼓楼区  5-10年   硕士                          ['临床数据分析', 'SAS', '生物统计']   \n",
       "5234   苏州-吴中区  5-10年   硕士                          ['临床数据分析', 'SAS', '生物统计']   \n",
       "2978   广州-越秀区  5-10年   硕士  ['SAS', 'SAP', 'SAR', '临床试验方案设计', '生物统计师', 'ED...   \n",
       "5874   长春-宽城区  5-10年   硕士                          ['临床数据分析', 'SAS', '生物统计']   \n",
       "5658   郑州-中原区  5-10年   硕士                          ['临床数据分析', 'SAS', '生物统计']   \n",
       "387    上海-徐汇区  5-10年   硕士                  ['Python', 'SQL', '回归分析', '聚类分析']   \n",
       "4887   深圳-龙华区  5-10年   硕士                          ['临床数据分析', 'SAS', '生物统计']   \n",
       "1806   北京-通州区  5-10年   本科                                         ['多元统计分析']   \n",
       "4880   深圳-南山区  5-10年   硕士                          ['临床数据分析', 'SAS', '生物统计']   \n",
       "6067   长沙-开福区  5-10年   硕士  ['SAS', 'SAP', 'SAR', '临床试验方案设计', '生物统计师', 'ED...   \n",
       "6262   青岛-市南区  5-10年   硕士                          ['临床数据分析', 'SAS', '生物统计']   \n",
       "797    上海-黄浦区  5-10年   本科                         ['行业研究', '市场研究', '市场信息收集']   \n",
       "531    上海-徐汇区    无经验   硕士        ['物理、数学', '量化分析', 'Python', '股票市场', '基金市场']   \n",
       "3992   武汉-江汉区  5-10年   硕士                          ['临床数据分析', 'SAS', '生物统计']   \n",
       "561    上海-长宁区  5-10年   硕士                          ['临床数据分析', 'SAS', '生物统计']   \n",
       "2094   南京-玄武区  5-10年   硕士                          ['临床数据分析', 'SAS', '生物统计']   \n",
       "2089   南京-鼓楼区  5-10年   硕士                          ['临床数据分析', 'SAS', '生物统计']   \n",
       "753   上海-浦东新区   1-3年   本科                                   ['期货研究', '证券研究']   \n",
       "1526   北京-海淀区  5-10年   本科                                            ['SQL']   \n",
       "194   上海-浦东新区   3-5年   本科  ['Python', 'SQL', 'Spark', 'MapReduce', 'Hadoo...   \n",
       "1602   北京-西城区   3-5年   本科                                  ['SQL', 'Hadoop']   \n",
       "1840   北京-朝阳区  5-10年   本科                        ['SQL', '线下零售', '生活服务/O2O']   \n",
       "218   上海-浦东新区   3-5年   本科                                  ['Python', 'SQL']   \n",
       "2992   广州-越秀区  5-10年   硕士                                 ['统计分析', '临床数据分析']   \n",
       "255        上海     不限   本科             ['商业数据分析', '电商数据分析', '游戏数据分析', 'Hive']   \n",
       "667    上海-徐汇区   3-5年   本科  ['金融', '量化分析', '证券从业资格证', '基金从业资格证', '证券数据分析',...   \n",
       "4760   深圳-南山区   3-5年   硕士                          ['数据分析', 'SQL', 'Python']   \n",
       "608    上海-长宁区  5-10年   本科                     ['SQL', 'Hive', 'Python', 'R']   \n",
       "6015   长沙-望城区   1-3年   本科  ['数据分析', '商业数据分析', '运营数据分析', '市场数据分析', '经营数据分析...   \n",
       "512    上海-徐汇区     不限   本科            ['生物统计学', '生物医疗', 'SQL', 'SAS', '数据管理']   \n",
       "\n",
       "                      公司名称    公司类型         公司规模  省份  城市      薪资上限      薪资下限  \\\n",
       "2030        江苏金恒信息科技股份有限公司  股份制企业     500-999人   江苏  南京  120000.0  200000.0   \n",
       "4721       江西省众灿互动科技股份有限公司     民营   1000-9999人   广东  深圳  100000.0  200000.0   \n",
       "361                   上海利真     民营   1000-9999人   上海  上海   80000.0  100000.0   \n",
       "4626         南京蓝魔云信息技术有限公司     民营       20-99人   广东  深圳   60000.0  100000.0   \n",
       "822        成都方向标企业管理咨询有限公司     民营       20-99人   上海  上海   60000.0   90000.0   \n",
       "487                   银科控股   外商独资   1000-9999人   上海  上海   60000.0   80000.0   \n",
       "3240  北京仕邦达人力资源服务有限公司上海分公司     合资     100-299人   四川  成都   50000.0   80000.0   \n",
       "2883         广东省国际工程咨询有限公司     国企     500-999人   广东  广州   40000.0   80000.0   \n",
       "1646        北京睿和良木管理咨询有限公司     民营     500-999人   北京  北京   40000.0   80000.0   \n",
       "3686        深圳今日人才信息科技有限公司     民营     100-299人   浙江  杭州   40000.0   80000.0   \n",
       "381           上海奥深商务咨询有限公司   外商独资     100-299人   上海  上海   45000.0   70000.0   \n",
       "1381        北京嘀嘀无限科技发展有限公司   外商独资   1000-9999人   北京  北京   45000.0   70000.0   \n",
       "595           北京上予医药科技有限公司     民营       20-99人   上海  上海   40000.0   70000.0   \n",
       "224                   满帮集团   上市公司   1000-9999人   上海  上海   40000.0   70000.0   \n",
       "437         华世先达咨询(北京)有限公司     民营     100-299人   上海  上海   40000.0   70000.0   \n",
       "853                   和铂医药  港澳台公司     100-299人   上海  上海   40000.0   70000.0   \n",
       "1456          北京健康之家科技有限公司     民营   1000-9999人   北京  北京   40000.0   70000.0   \n",
       "4834        华世先达咨询(北京)有限公司     民营     100-299人   广东  深圳   35000.0   70000.0   \n",
       "5821        深圳今日人才信息科技有限公司     民营     100-299人   重庆  重庆   35000.0   70000.0   \n",
       "1763                  麦科奥特     合资       20-99人   北京  北京   35000.0   70000.0   \n",
       "5104        全益(上海)人力资源有限公司     民营       20-99人   福建  福州   40000.0   60000.0   \n",
       "5234        全益(上海)人力资源有限公司     民营       20-99人   江苏  苏州   40000.0   60000.0   \n",
       "2978          深圳高邦信息技术有限公司     民营       20-99人   广东  广州   40000.0   60000.0   \n",
       "5874        全益(上海)人力资源有限公司     民营       20-99人   吉林  长春   40000.0   60000.0   \n",
       "5658        全益(上海)人力资源有限公司     民营       20-99人   河南  郑州   40000.0   60000.0   \n",
       "387          湖北斯克斯人力资源有限公司     民营     300-499人   上海  上海   40000.0   60000.0   \n",
       "4887        全益(上海)人力资源有限公司     民营       20-99人   广东  深圳   40000.0   60000.0   \n",
       "1806        北京联东投资（集团）有限公司     民营   1000-9999人   北京  北京   40000.0   60000.0   \n",
       "4880        全益(上海)人力资源有限公司     民营       20-99人   广东  深圳   40000.0   60000.0   \n",
       "6067          深圳高邦信息技术有限公司     民营       20-99人   湖南  长沙   40000.0   60000.0   \n",
       "6262        全益(上海)人力资源有限公司     民营       20-99人   山东  青岛   40000.0   60000.0   \n",
       "797          北京大北农贸易有限责任公司                20人以下  上海  上海   40000.0   60000.0   \n",
       "531         蓝泉(徐州)信息科技有限公司     民营     100-299人   上海  上海   40000.0   60000.0   \n",
       "3992        全益(上海)人力资源有限公司     民营       20-99人   湖北  武汉   40000.0   60000.0   \n",
       "561         全益(上海)人力资源有限公司     民营       20-99人   上海  上海   40000.0   60000.0   \n",
       "2094        全益(上海)人力资源有限公司     民营       20-99人   江苏  南京   40000.0   60000.0   \n",
       "2089        全益(上海)人力资源有限公司     民营       20-99人   江苏  南京   40000.0   60000.0   \n",
       "753        深圳市昱辰宽客管理咨询有限公司     民营        20人以下   上海  上海   40000.0   60000.0   \n",
       "1526      北京福睿思特国际人力资源有限公司     民营       20-99人   北京  北京   35000.0   60000.0   \n",
       "194         中邮邮惠万家银行有限责任公司     国企     100-299人   上海  上海   30000.0   60000.0   \n",
       "1602           亨特爾獵頭(中國)公司   外商独资     100-299人   北京  北京   30000.0   60000.0   \n",
       "1840     卓一嘉越管理咨询(连云港)有限公司  股份制企业       20-99人   北京  北京   30000.0   60000.0   \n",
       "218            亨特爾獵頭(中國)公司   外商独资     100-299人   上海  上海   30000.0   60000.0   \n",
       "2992                  联邦制药   上市公司     10000人以上   广东  广州   30000.0   60000.0   \n",
       "255                    拼多多     民营   1000-9999人   上海  上海   30000.0   60000.0   \n",
       "667         蓝泉(徐州)信息科技有限公司     民营     100-299人   上海  上海   30000.0   60000.0   \n",
       "4760                   展动力     民营   1000-9999人   广东  深圳   30000.0   60000.0   \n",
       "608     安拓奥古(北京)人力资源服务有限公司     合资     300-499人   上海  上海   35000.0   55000.0   \n",
       "6015        西双版纳创优餐饮管理有限公司     民营   1000-9999人   湖南  长沙   40000.0   50000.0   \n",
       "512             上海沛生企业管理中心     合资       20-99人   上海  上海   30000.0   60000.0   \n",
       "\n",
       "      处理后工作经验      薪资均值  \n",
       "2030      4.0  160000.0  \n",
       "4721      2.0  150000.0  \n",
       "361       2.0   90000.0  \n",
       "4626      7.5   80000.0  \n",
       "822       7.5   75000.0  \n",
       "487       7.5   70000.0  \n",
       "3240      7.5   65000.0  \n",
       "2883      7.5   60000.0  \n",
       "1646      7.5   60000.0  \n",
       "3686      7.5   60000.0  \n",
       "381       7.5   57500.0  \n",
       "1381      7.5   57500.0  \n",
       "595       4.0   55000.0  \n",
       "224       7.5   55000.0  \n",
       "437       4.0   55000.0  \n",
       "853      10.0   55000.0  \n",
       "1456      7.5   55000.0  \n",
       "4834      4.0   52500.0  \n",
       "5821      7.5   52500.0  \n",
       "1763      2.0   52500.0  \n",
       "5104      7.5   50000.0  \n",
       "5234      7.5   50000.0  \n",
       "2978      7.5   50000.0  \n",
       "5874      7.5   50000.0  \n",
       "5658      7.5   50000.0  \n",
       "387       7.5   50000.0  \n",
       "4887      7.5   50000.0  \n",
       "1806      7.5   50000.0  \n",
       "4880      7.5   50000.0  \n",
       "6067      7.5   50000.0  \n",
       "6262      7.5   50000.0  \n",
       "797       7.5   50000.0  \n",
       "531       0.0   50000.0  \n",
       "3992      7.5   50000.0  \n",
       "561       7.5   50000.0  \n",
       "2094      7.5   50000.0  \n",
       "2089      7.5   50000.0  \n",
       "753       2.0   50000.0  \n",
       "1526      7.5   47500.0  \n",
       "194       4.0   45000.0  \n",
       "1602      4.0   45000.0  \n",
       "1840      7.5   45000.0  \n",
       "218       4.0   45000.0  \n",
       "2992      7.5   45000.0  \n",
       "255       1.0   45000.0  \n",
       "667       4.0   45000.0  \n",
       "4760      4.0   45000.0  \n",
       "608       7.5   45000.0  \n",
       "6015      2.0   45000.0  \n",
       "512       1.0   45000.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按照薪资均值降序排序\n",
    "sorted_data = data2.sort_values(by='薪资均值', ascending=False)\n",
    "\n",
    "# 获取前50个企业\n",
    "sorted_data.head(50)\n"
   ]
  }
 ],
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